{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"1决策树.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"-vdyeNz2mpP4","colab_type":"text"},"source":["# 分类树"]},{"cell_type":"markdown","metadata":{"id":"T6Wu0j97m4VA","colab_type":"text"},"source":["## 红酒数据集"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"G2YYezW4mz43","colab":{}},"source":["from sklearn import tree\n","from sklearn.datasets import load_wine\n","from sklearn.model_selection import train_test_split"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oKaWOcJ8pqUB","colab_type":"code","outputId":"fe16612e-2dc2-4627-c293-a9857db46749","executionInfo":{"status":"ok","timestamp":1545963580103,"user_tz":-480,"elapsed":720,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":685}},"source":["wine = load_wine()\n","wine\n","# 字典的形式"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n',\n"," 'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n","         1.065e+03],\n","        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n","         1.050e+03],\n","        [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n","         1.185e+03],\n","        ...,\n","        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n","         8.350e+02],\n","        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n","         8.400e+02],\n","        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n","         5.600e+02]]),\n"," 'feature_names': ['alcohol',\n","  'malic_acid',\n","  'ash',\n","  'alcalinity_of_ash',\n","  'magnesium',\n","  'total_phenols',\n","  'flavanoids',\n","  'nonflavanoid_phenols',\n","  'proanthocyanins',\n","  'color_intensity',\n","  'hue',\n","  'od280/od315_of_diluted_wines',\n","  'proline'],\n"," 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n","        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2]),\n"," 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7')}"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"DRvbFfllpwoq","colab_type":"code","outputId":"ca69c706-3721-42ff-c6e6-47403eb94dbd","executionInfo":{"status":"ok","timestamp":1545963598428,"user_tz":-480,"elapsed":743,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["wine.data.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(178, 13)"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"mjfygw6Gp2aj","colab_type":"code","outputId":"ccd543d1-4794-4847-e0cd-3078af36056a","executionInfo":{"status":"ok","timestamp":1545963600569,"user_tz":-480,"elapsed":732,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":175}},"source":["wine.target"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n","       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","       2, 2])"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"oN92xylDp4pv","colab_type":"code","outputId":"a3747fa6-8b51-4b8a-8842-74e2d6d4e706","executionInfo":{"status":"ok","timestamp":1545963604512,"user_tz":-480,"elapsed":699,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":2062}},"source":["import pandas as pd\n","pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)],axis=1)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>14.23</td>\n","      <td>1.71</td>\n","      <td>2.43</td>\n","      <td>15.6</td>\n","      <td>127.0</td>\n","      <td>2.80</td>\n","      <td>3.06</td>\n","      <td>0.28</td>\n","      <td>2.29</td>\n","      <td>5.640000</td>\n","      <td>1.04</td>\n","      <td>3.92</td>\n","      <td>1065.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>13.20</td>\n","      <td>1.78</td>\n","      <td>2.14</td>\n","      <td>11.2</td>\n","      <td>100.0</td>\n","      <td>2.65</td>\n","      <td>2.76</td>\n","      <td>0.26</td>\n","      <td>1.28</td>\n","      <td>4.380000</td>\n","      <td>1.05</td>\n","      <td>3.40</td>\n","      <td>1050.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>13.16</td>\n","      <td>2.36</td>\n","      <td>2.67</td>\n","      <td>18.6</td>\n","      <td>101.0</td>\n","      <td>2.80</td>\n","      <td>3.24</td>\n","      <td>0.30</td>\n","      <td>2.81</td>\n","      <td>5.680000</td>\n","      <td>1.03</td>\n","      <td>3.17</td>\n","      <td>1185.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>14.37</td>\n","      <td>1.95</td>\n","      <td>2.50</td>\n","      <td>16.8</td>\n","      <td>113.0</td>\n","      <td>3.85</td>\n","      <td>3.49</td>\n","      <td>0.24</td>\n","      <td>2.18</td>\n","      <td>7.800000</td>\n","      <td>0.86</td>\n","      <td>3.45</td>\n","      <td>1480.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>13.24</td>\n","      <td>2.59</td>\n","      <td>2.87</td>\n","      <td>21.0</td>\n","      <td>118.0</td>\n","      <td>2.80</td>\n","      <td>2.69</td>\n","      <td>0.39</td>\n","      <td>1.82</td>\n","      <td>4.320000</td>\n","      <td>1.04</td>\n","      <td>2.93</td>\n","      <td>735.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>14.20</td>\n","      <td>1.76</td>\n","      <td>2.45</td>\n","      <td>15.2</td>\n","      <td>112.0</td>\n","      <td>3.27</td>\n","      <td>3.39</td>\n","      <td>0.34</td>\n","      <td>1.97</td>\n","      <td>6.750000</td>\n","      <td>1.05</td>\n","      <td>2.85</td>\n","      <td>1450.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>14.39</td>\n","      <td>1.87</td>\n","      <td>2.45</td>\n","      <td>14.6</td>\n","      <td>96.0</td>\n","      <td>2.50</td>\n","      <td>2.52</td>\n","      <td>0.30</td>\n","      <td>1.98</td>\n","      <td>5.250000</td>\n","      <td>1.02</td>\n","      <td>3.58</td>\n","      <td>1290.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>14.06</td>\n","      <td>2.15</td>\n","      <td>2.61</td>\n","      <td>17.6</td>\n","      <td>121.0</td>\n","      <td>2.60</td>\n","      <td>2.51</td>\n","      <td>0.31</td>\n","      <td>1.25</td>\n","      <td>5.050000</td>\n","      <td>1.06</td>\n","      <td>3.58</td>\n","      <td>1295.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>14.83</td>\n","      <td>1.64</td>\n","      <td>2.17</td>\n","      <td>14.0</td>\n","      <td>97.0</td>\n","      <td>2.80</td>\n","      <td>2.98</td>\n","      <td>0.29</td>\n","      <td>1.98</td>\n","      <td>5.200000</td>\n","      <td>1.08</td>\n","      <td>2.85</td>\n","      <td>1045.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>13.86</td>\n","      <td>1.35</td>\n","      <td>2.27</td>\n","      <td>16.0</td>\n","      <td>98.0</td>\n","      <td>2.98</td>\n","      <td>3.15</td>\n","      <td>0.22</td>\n","      <td>1.85</td>\n","      <td>7.220000</td>\n","      <td>1.01</td>\n","      <td>3.55</td>\n","      <td>1045.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>14.10</td>\n","      <td>2.16</td>\n","      <td>2.30</td>\n","      <td>18.0</td>\n","      <td>105.0</td>\n","      <td>2.95</td>\n","      <td>3.32</td>\n","      <td>0.22</td>\n","      <td>2.38</td>\n","      <td>5.750000</td>\n","      <td>1.25</td>\n","      <td>3.17</td>\n","      <td>1510.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>14.12</td>\n","      <td>1.48</td>\n","      <td>2.32</td>\n","      <td>16.8</td>\n","      <td>95.0</td>\n","      <td>2.20</td>\n","      <td>2.43</td>\n","      <td>0.26</td>\n","      <td>1.57</td>\n","      <td>5.000000</td>\n","      <td>1.17</td>\n","      <td>2.82</td>\n","      <td>1280.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>13.75</td>\n","      <td>1.73</td>\n","      <td>2.41</td>\n","      <td>16.0</td>\n","      <td>89.0</td>\n","      <td>2.60</td>\n","      <td>2.76</td>\n","      <td>0.29</td>\n","      <td>1.81</td>\n","      <td>5.600000</td>\n","      <td>1.15</td>\n","      <td>2.90</td>\n","      <td>1320.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>14.75</td>\n","      <td>1.73</td>\n","      <td>2.39</td>\n","      <td>11.4</td>\n","      <td>91.0</td>\n","      <td>3.10</td>\n","      <td>3.69</td>\n","      <td>0.43</td>\n","      <td>2.81</td>\n","      <td>5.400000</td>\n","      <td>1.25</td>\n","      <td>2.73</td>\n","      <td>1150.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>14.38</td>\n","      <td>1.87</td>\n","      <td>2.38</td>\n","      <td>12.0</td>\n","      <td>102.0</td>\n","      <td>3.30</td>\n","      <td>3.64</td>\n","      <td>0.29</td>\n","      <td>2.96</td>\n","      <td>7.500000</td>\n","      <td>1.20</td>\n","      <td>3.00</td>\n","      <td>1547.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>13.63</td>\n","      <td>1.81</td>\n","      <td>2.70</td>\n","      <td>17.2</td>\n","      <td>112.0</td>\n","      <td>2.85</td>\n","      <td>2.91</td>\n","      <td>0.30</td>\n","      <td>1.46</td>\n","      <td>7.300000</td>\n","      <td>1.28</td>\n","      <td>2.88</td>\n","      <td>1310.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>14.30</td>\n","      <td>1.92</td>\n","      <td>2.72</td>\n","      <td>20.0</td>\n","      <td>120.0</td>\n","      <td>2.80</td>\n","      <td>3.14</td>\n","      <td>0.33</td>\n","      <td>1.97</td>\n","      <td>6.200000</td>\n","      <td>1.07</td>\n","      <td>2.65</td>\n","      <td>1280.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>13.83</td>\n","      <td>1.57</td>\n","      <td>2.62</td>\n","      <td>20.0</td>\n","      <td>115.0</td>\n","      <td>2.95</td>\n","      <td>3.40</td>\n","      <td>0.40</td>\n","      <td>1.72</td>\n","      <td>6.600000</td>\n","      <td>1.13</td>\n","      <td>2.57</td>\n","      <td>1130.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>14.19</td>\n","      <td>1.59</td>\n","      <td>2.48</td>\n","      <td>16.5</td>\n","      <td>108.0</td>\n","      <td>3.30</td>\n","      <td>3.93</td>\n","      <td>0.32</td>\n","      <td>1.86</td>\n","      <td>8.700000</td>\n","      <td>1.23</td>\n","      <td>2.82</td>\n","      <td>1680.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>13.64</td>\n","      <td>3.10</td>\n","      <td>2.56</td>\n","      <td>15.2</td>\n","      <td>116.0</td>\n","      <td>2.70</td>\n","      <td>3.03</td>\n","      <td>0.17</td>\n","      <td>1.66</td>\n","      <td>5.100000</td>\n","      <td>0.96</td>\n","      <td>3.36</td>\n","      <td>845.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>14.06</td>\n","      <td>1.63</td>\n","      <td>2.28</td>\n","      <td>16.0</td>\n","      <td>126.0</td>\n","      <td>3.00</td>\n","      <td>3.17</td>\n","      <td>0.24</td>\n","      <td>2.10</td>\n","      <td>5.650000</td>\n","      <td>1.09</td>\n","      <td>3.71</td>\n","      <td>780.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>12.93</td>\n","      <td>3.80</td>\n","      <td>2.65</td>\n","      <td>18.6</td>\n","      <td>102.0</td>\n","      <td>2.41</td>\n","      <td>2.41</td>\n","      <td>0.25</td>\n","      <td>1.98</td>\n","      <td>4.500000</td>\n","      <td>1.03</td>\n","      <td>3.52</td>\n","      <td>770.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>13.71</td>\n","      <td>1.86</td>\n","      <td>2.36</td>\n","      <td>16.6</td>\n","      <td>101.0</td>\n","      <td>2.61</td>\n","      <td>2.88</td>\n","      <td>0.27</td>\n","      <td>1.69</td>\n","      <td>3.800000</td>\n","      <td>1.11</td>\n","      <td>4.00</td>\n","      <td>1035.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>12.85</td>\n","      <td>1.60</td>\n","      <td>2.52</td>\n","      <td>17.8</td>\n","      <td>95.0</td>\n","      <td>2.48</td>\n","      <td>2.37</td>\n","      <td>0.26</td>\n","      <td>1.46</td>\n","      <td>3.930000</td>\n","      <td>1.09</td>\n","      <td>3.63</td>\n","      <td>1015.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>13.50</td>\n","      <td>1.81</td>\n","      <td>2.61</td>\n","      <td>20.0</td>\n","      <td>96.0</td>\n","      <td>2.53</td>\n","      <td>2.61</td>\n","      <td>0.28</td>\n","      <td>1.66</td>\n","      <td>3.520000</td>\n","      <td>1.12</td>\n","      <td>3.82</td>\n","      <td>845.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>13.05</td>\n","      <td>2.05</td>\n","      <td>3.22</td>\n","      <td>25.0</td>\n","      <td>124.0</td>\n","      <td>2.63</td>\n","      <td>2.68</td>\n","      <td>0.47</td>\n","      <td>1.92</td>\n","      <td>3.580000</td>\n","      <td>1.13</td>\n","      <td>3.20</td>\n","      <td>830.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>13.39</td>\n","      <td>1.77</td>\n","      <td>2.62</td>\n","      <td>16.1</td>\n","      <td>93.0</td>\n","      <td>2.85</td>\n","      <td>2.94</td>\n","      <td>0.34</td>\n","      <td>1.45</td>\n","      <td>4.800000</td>\n","      <td>0.92</td>\n","      <td>3.22</td>\n","      <td>1195.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>13.30</td>\n","      <td>1.72</td>\n","      <td>2.14</td>\n","      <td>17.0</td>\n","      <td>94.0</td>\n","      <td>2.40</td>\n","      <td>2.19</td>\n","      <td>0.27</td>\n","      <td>1.35</td>\n","      <td>3.950000</td>\n","      <td>1.02</td>\n","      <td>2.77</td>\n","      <td>1285.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>13.87</td>\n","      <td>1.90</td>\n","      <td>2.80</td>\n","      <td>19.4</td>\n","      <td>107.0</td>\n","      <td>2.95</td>\n","      <td>2.97</td>\n","      <td>0.37</td>\n","      <td>1.76</td>\n","      <td>4.500000</td>\n","      <td>1.25</td>\n","      <td>3.40</td>\n","      <td>915.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>14.02</td>\n","      <td>1.68</td>\n","      <td>2.21</td>\n","      <td>16.0</td>\n","      <td>96.0</td>\n","      <td>2.65</td>\n","      <td>2.33</td>\n","      <td>0.26</td>\n","      <td>1.98</td>\n","      <td>4.700000</td>\n","      <td>1.04</td>\n","      <td>3.59</td>\n","      <td>1035.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>13.32</td>\n","      <td>3.24</td>\n","      <td>2.38</td>\n","      <td>21.5</td>\n","      <td>92.0</td>\n","      <td>1.93</td>\n","      <td>0.76</td>\n","      <td>0.45</td>\n","      <td>1.25</td>\n","      <td>8.420000</td>\n","      <td>0.55</td>\n","      <td>1.62</td>\n","      <td>650.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>13.08</td>\n","      <td>3.90</td>\n","      <td>2.36</td>\n","      <td>21.5</td>\n","      <td>113.0</td>\n","      <td>1.41</td>\n","      <td>1.39</td>\n","      <td>0.34</td>\n","      <td>1.14</td>\n","      <td>9.400000</td>\n","      <td>0.57</td>\n","      <td>1.33</td>\n","      <td>550.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>150</th>\n","      <td>13.50</td>\n","      <td>3.12</td>\n","      <td>2.62</td>\n","      <td>24.0</td>\n","      <td>123.0</td>\n","      <td>1.40</td>\n","      <td>1.57</td>\n","      <td>0.22</td>\n","      <td>1.25</td>\n","      <td>8.600000</td>\n","      <td>0.59</td>\n","      <td>1.30</td>\n","      <td>500.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>151</th>\n","      <td>12.79</td>\n","      <td>2.67</td>\n","      <td>2.48</td>\n","      <td>22.0</td>\n","      <td>112.0</td>\n","      <td>1.48</td>\n","      <td>1.36</td>\n","      <td>0.24</td>\n","      <td>1.26</td>\n","      <td>10.800000</td>\n","      <td>0.48</td>\n","      <td>1.47</td>\n","      <td>480.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>152</th>\n","      <td>13.11</td>\n","      <td>1.90</td>\n","      <td>2.75</td>\n","      <td>25.5</td>\n","      <td>116.0</td>\n","      <td>2.20</td>\n","      <td>1.28</td>\n","      <td>0.26</td>\n","      <td>1.56</td>\n","      <td>7.100000</td>\n","      <td>0.61</td>\n","      <td>1.33</td>\n","      <td>425.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>153</th>\n","      <td>13.23</td>\n","      <td>3.30</td>\n","      <td>2.28</td>\n","      <td>18.5</td>\n","      <td>98.0</td>\n","      <td>1.80</td>\n","      <td>0.83</td>\n","      <td>0.61</td>\n","      <td>1.87</td>\n","      <td>10.520000</td>\n","      <td>0.56</td>\n","      <td>1.51</td>\n","      <td>675.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>154</th>\n","      <td>12.58</td>\n","      <td>1.29</td>\n","      <td>2.10</td>\n","      <td>20.0</td>\n","      <td>103.0</td>\n","      <td>1.48</td>\n","      <td>0.58</td>\n","      <td>0.53</td>\n","      <td>1.40</td>\n","      <td>7.600000</td>\n","      <td>0.58</td>\n","      <td>1.55</td>\n","      <td>640.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>155</th>\n","      <td>13.17</td>\n","      <td>5.19</td>\n","      <td>2.32</td>\n","      <td>22.0</td>\n","      <td>93.0</td>\n","      <td>1.74</td>\n","      <td>0.63</td>\n","      <td>0.61</td>\n","      <td>1.55</td>\n","      <td>7.900000</td>\n","      <td>0.60</td>\n","      <td>1.48</td>\n","      <td>725.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>156</th>\n","      <td>13.84</td>\n","      <td>4.12</td>\n","      <td>2.38</td>\n","      <td>19.5</td>\n","      <td>89.0</td>\n","      <td>1.80</td>\n","      <td>0.83</td>\n","      <td>0.48</td>\n","      <td>1.56</td>\n","      <td>9.010000</td>\n","      <td>0.57</td>\n","      <td>1.64</td>\n","      <td>480.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>157</th>\n","      <td>12.45</td>\n","      <td>3.03</td>\n","      <td>2.64</td>\n","      <td>27.0</td>\n","      <td>97.0</td>\n","      <td>1.90</td>\n","      <td>0.58</td>\n","      <td>0.63</td>\n","      <td>1.14</td>\n","      <td>7.500000</td>\n","      <td>0.67</td>\n","      <td>1.73</td>\n","      <td>880.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>158</th>\n","      <td>14.34</td>\n","      <td>1.68</td>\n","      <td>2.70</td>\n","      <td>25.0</td>\n","      <td>98.0</td>\n","      <td>2.80</td>\n","      <td>1.31</td>\n","      <td>0.53</td>\n","      <td>2.70</td>\n","      <td>13.000000</td>\n","      <td>0.57</td>\n","      <td>1.96</td>\n","      <td>660.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>159</th>\n","      <td>13.48</td>\n","      <td>1.67</td>\n","      <td>2.64</td>\n","      <td>22.5</td>\n","      <td>89.0</td>\n","      <td>2.60</td>\n","      <td>1.10</td>\n","      <td>0.52</td>\n","      <td>2.29</td>\n","      <td>11.750000</td>\n","      <td>0.57</td>\n","      <td>1.78</td>\n","      <td>620.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>160</th>\n","      <td>12.36</td>\n","      <td>3.83</td>\n","      <td>2.38</td>\n","      <td>21.0</td>\n","      <td>88.0</td>\n","      <td>2.30</td>\n","      <td>0.92</td>\n","      <td>0.50</td>\n","      <td>1.04</td>\n","      <td>7.650000</td>\n","      <td>0.56</td>\n","      <td>1.58</td>\n","      <td>520.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>161</th>\n","      <td>13.69</td>\n","      <td>3.26</td>\n","      <td>2.54</td>\n","      <td>20.0</td>\n","      <td>107.0</td>\n","      <td>1.83</td>\n","      <td>0.56</td>\n","      <td>0.50</td>\n","      <td>0.80</td>\n","      <td>5.880000</td>\n","      <td>0.96</td>\n","      <td>1.82</td>\n","      <td>680.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>162</th>\n","      <td>12.85</td>\n","      <td>3.27</td>\n","      <td>2.58</td>\n","      <td>22.0</td>\n","      <td>106.0</td>\n","      <td>1.65</td>\n","      <td>0.60</td>\n","      <td>0.60</td>\n","      <td>0.96</td>\n","      <td>5.580000</td>\n","      <td>0.87</td>\n","      <td>2.11</td>\n","      <td>570.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>163</th>\n","      <td>12.96</td>\n","      <td>3.45</td>\n","      <td>2.35</td>\n","      <td>18.5</td>\n","      <td>106.0</td>\n","      <td>1.39</td>\n","      <td>0.70</td>\n","      <td>0.40</td>\n","      <td>0.94</td>\n","      <td>5.280000</td>\n","      <td>0.68</td>\n","      <td>1.75</td>\n","      <td>675.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>164</th>\n","      <td>13.78</td>\n","      <td>2.76</td>\n","      <td>2.30</td>\n","      <td>22.0</td>\n","      <td>90.0</td>\n","      <td>1.35</td>\n","      <td>0.68</td>\n","      <td>0.41</td>\n","      <td>1.03</td>\n","      <td>9.580000</td>\n","      <td>0.70</td>\n","      <td>1.68</td>\n","      <td>615.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>165</th>\n","      <td>13.73</td>\n","      <td>4.36</td>\n","      <td>2.26</td>\n","      <td>22.5</td>\n","      <td>88.0</td>\n","      <td>1.28</td>\n","      <td>0.47</td>\n","      <td>0.52</td>\n","      <td>1.15</td>\n","      <td>6.620000</td>\n","      <td>0.78</td>\n","      <td>1.75</td>\n","      <td>520.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>166</th>\n","      <td>13.45</td>\n","      <td>3.70</td>\n","      <td>2.60</td>\n","      <td>23.0</td>\n","      <td>111.0</td>\n","      <td>1.70</td>\n","      <td>0.92</td>\n","      <td>0.43</td>\n","      <td>1.46</td>\n","      <td>10.680000</td>\n","      <td>0.85</td>\n","      <td>1.56</td>\n","      <td>695.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>167</th>\n","      <td>12.82</td>\n","      <td>3.37</td>\n","      <td>2.30</td>\n","      <td>19.5</td>\n","      <td>88.0</td>\n","      <td>1.48</td>\n","      <td>0.66</td>\n","      <td>0.40</td>\n","      <td>0.97</td>\n","      <td>10.260000</td>\n","      <td>0.72</td>\n","      <td>1.75</td>\n","      <td>685.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>168</th>\n","      <td>13.58</td>\n","      <td>2.58</td>\n","      <td>2.69</td>\n","      <td>24.5</td>\n","      <td>105.0</td>\n","      <td>1.55</td>\n","      <td>0.84</td>\n","      <td>0.39</td>\n","      <td>1.54</td>\n","      <td>8.660000</td>\n","      <td>0.74</td>\n","      <td>1.80</td>\n","      <td>750.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>169</th>\n","      <td>13.40</td>\n","      <td>4.60</td>\n","      <td>2.86</td>\n","      <td>25.0</td>\n","      <td>112.0</td>\n","      <td>1.98</td>\n","      <td>0.96</td>\n","      <td>0.27</td>\n","      <td>1.11</td>\n","      <td>8.500000</td>\n","      <td>0.67</td>\n","      <td>1.92</td>\n","      <td>630.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>170</th>\n","      <td>12.20</td>\n","      <td>3.03</td>\n","      <td>2.32</td>\n","      <td>19.0</td>\n","      <td>96.0</td>\n","      <td>1.25</td>\n","      <td>0.49</td>\n","      <td>0.40</td>\n","      <td>0.73</td>\n","      <td>5.500000</td>\n","      <td>0.66</td>\n","      <td>1.83</td>\n","      <td>510.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>171</th>\n","      <td>12.77</td>\n","      <td>2.39</td>\n","      <td>2.28</td>\n","      <td>19.5</td>\n","      <td>86.0</td>\n","      <td>1.39</td>\n","      <td>0.51</td>\n","      <td>0.48</td>\n","      <td>0.64</td>\n","      <td>9.899999</td>\n","      <td>0.57</td>\n","      <td>1.63</td>\n","      <td>470.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>172</th>\n","      <td>14.16</td>\n","      <td>2.51</td>\n","      <td>2.48</td>\n","      <td>20.0</td>\n","      <td>91.0</td>\n","      <td>1.68</td>\n","      <td>0.70</td>\n","      <td>0.44</td>\n","      <td>1.24</td>\n","      <td>9.700000</td>\n","      <td>0.62</td>\n","      <td>1.71</td>\n","      <td>660.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>173</th>\n","      <td>13.71</td>\n","      <td>5.65</td>\n","      <td>2.45</td>\n","      <td>20.5</td>\n","      <td>95.0</td>\n","      <td>1.68</td>\n","      <td>0.61</td>\n","      <td>0.52</td>\n","      <td>1.06</td>\n","      <td>7.700000</td>\n","      <td>0.64</td>\n","      <td>1.74</td>\n","      <td>740.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>174</th>\n","      <td>13.40</td>\n","      <td>3.91</td>\n","      <td>2.48</td>\n","      <td>23.0</td>\n","      <td>102.0</td>\n","      <td>1.80</td>\n","      <td>0.75</td>\n","      <td>0.43</td>\n","      <td>1.41</td>\n","      <td>7.300000</td>\n","      <td>0.70</td>\n","      <td>1.56</td>\n","      <td>750.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>175</th>\n","      <td>13.27</td>\n","      <td>4.28</td>\n","      <td>2.26</td>\n","      <td>20.0</td>\n","      <td>120.0</td>\n","      <td>1.59</td>\n","      <td>0.69</td>\n","      <td>0.43</td>\n","      <td>1.35</td>\n","      <td>10.200000</td>\n","      <td>0.59</td>\n","      <td>1.56</td>\n","      <td>835.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>176</th>\n","      <td>13.17</td>\n","      <td>2.59</td>\n","      <td>2.37</td>\n","      <td>20.0</td>\n","      <td>120.0</td>\n","      <td>1.65</td>\n","      <td>0.68</td>\n","      <td>0.53</td>\n","      <td>1.46</td>\n","      <td>9.300000</td>\n","      <td>0.60</td>\n","      <td>1.62</td>\n","      <td>840.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>177</th>\n","      <td>14.13</td>\n","      <td>4.10</td>\n","      <td>2.74</td>\n","      <td>24.5</td>\n","      <td>96.0</td>\n","      <td>2.05</td>\n","      <td>0.76</td>\n","      <td>0.56</td>\n","      <td>1.35</td>\n","      <td>9.200000</td>\n","      <td>0.61</td>\n","      <td>1.60</td>\n","      <td>560.0</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>178 rows × 14 columns</p>\n","</div>"],"text/plain":["        0     1     2     3      4     5     6     7     8          9     10  \\\n","0    14.23  1.71  2.43  15.6  127.0  2.80  3.06  0.28  2.29   5.640000  1.04   \n","1    13.20  1.78  2.14  11.2  100.0  2.65  2.76  0.26  1.28   4.380000  1.05   \n","2    13.16  2.36  2.67  18.6  101.0  2.80  3.24  0.30  2.81   5.680000  1.03   \n","3    14.37  1.95  2.50  16.8  113.0  3.85  3.49  0.24  2.18   7.800000  0.86   \n","4    13.24  2.59  2.87  21.0  118.0  2.80  2.69  0.39  1.82   4.320000  1.04   \n","5    14.20  1.76  2.45  15.2  112.0  3.27  3.39  0.34  1.97   6.750000  1.05   \n","6    14.39  1.87  2.45  14.6   96.0  2.50  2.52  0.30  1.98   5.250000  1.02   \n","7    14.06  2.15  2.61  17.6  121.0  2.60  2.51  0.31  1.25   5.050000  1.06   \n","8    14.83  1.64  2.17  14.0   97.0  2.80  2.98  0.29  1.98   5.200000  1.08   \n","9    13.86  1.35  2.27  16.0   98.0  2.98  3.15  0.22  1.85   7.220000  1.01   \n","10   14.10  2.16  2.30  18.0  105.0  2.95  3.32  0.22  2.38   5.750000  1.25   \n","11   14.12  1.48  2.32  16.8   95.0  2.20  2.43  0.26  1.57   5.000000  1.17   \n","12   13.75  1.73  2.41  16.0   89.0  2.60  2.76  0.29  1.81   5.600000  1.15   \n","13   14.75  1.73  2.39  11.4   91.0  3.10  3.69  0.43  2.81   5.400000  1.25   \n","14   14.38  1.87  2.38  12.0  102.0  3.30  3.64  0.29  2.96   7.500000  1.20   \n","15   13.63  1.81  2.70  17.2  112.0  2.85  2.91  0.30  1.46   7.300000  1.28   \n","16   14.30  1.92  2.72  20.0  120.0  2.80  3.14  0.33  1.97   6.200000  1.07   \n","17   13.83  1.57  2.62  20.0  115.0  2.95  3.40  0.40  1.72   6.600000  1.13   \n","18   14.19  1.59  2.48  16.5  108.0  3.30  3.93  0.32  1.86   8.700000  1.23   \n","19   13.64  3.10  2.56  15.2  116.0  2.70  3.03  0.17  1.66   5.100000  0.96   \n","20   14.06  1.63  2.28  16.0  126.0  3.00  3.17  0.24  2.10   5.650000  1.09   \n","21   12.93  3.80  2.65  18.6  102.0  2.41  2.41  0.25  1.98   4.500000  1.03   \n","22   13.71  1.86  2.36  16.6  101.0  2.61  2.88  0.27  1.69   3.800000  1.11   \n","23   12.85  1.60  2.52  17.8   95.0  2.48  2.37  0.26  1.46   3.930000  1.09   \n","24   13.50  1.81  2.61  20.0   96.0  2.53  2.61  0.28  1.66   3.520000  1.12   \n","25   13.05  2.05  3.22  25.0  124.0  2.63  2.68  0.47  1.92   3.580000  1.13   \n","26   13.39  1.77  2.62  16.1   93.0  2.85  2.94  0.34  1.45   4.800000  0.92   \n","27   13.30  1.72  2.14  17.0   94.0  2.40  2.19  0.27  1.35   3.950000  1.02   \n","28   13.87  1.90  2.80  19.4  107.0  2.95  2.97  0.37  1.76   4.500000  1.25   \n","29   14.02  1.68  2.21  16.0   96.0  2.65  2.33  0.26  1.98   4.700000  1.04   \n","..     ...   ...   ...   ...    ...   ...   ...   ...   ...        ...   ...   \n","148  13.32  3.24  2.38  21.5   92.0  1.93  0.76  0.45  1.25   8.420000  0.55   \n","149  13.08  3.90  2.36  21.5  113.0  1.41  1.39  0.34  1.14   9.400000  0.57   \n","150  13.50  3.12  2.62  24.0  123.0  1.40  1.57  0.22  1.25   8.600000  0.59   \n","151  12.79  2.67  2.48  22.0  112.0  1.48  1.36  0.24  1.26  10.800000  0.48   \n","152  13.11  1.90  2.75  25.5  116.0  2.20  1.28  0.26  1.56   7.100000  0.61   \n","153  13.23  3.30  2.28  18.5   98.0  1.80  0.83  0.61  1.87  10.520000  0.56   \n","154  12.58  1.29  2.10  20.0  103.0  1.48  0.58  0.53  1.40   7.600000  0.58   \n","155  13.17  5.19  2.32  22.0   93.0  1.74  0.63  0.61  1.55   7.900000  0.60   \n","156  13.84  4.12  2.38  19.5   89.0  1.80  0.83  0.48  1.56   9.010000  0.57   \n","157  12.45  3.03  2.64  27.0   97.0  1.90  0.58  0.63  1.14   7.500000  0.67   \n","158  14.34  1.68  2.70  25.0   98.0  2.80  1.31  0.53  2.70  13.000000  0.57   \n","159  13.48  1.67  2.64  22.5   89.0  2.60  1.10  0.52  2.29  11.750000  0.57   \n","160  12.36  3.83  2.38  21.0   88.0  2.30  0.92  0.50  1.04   7.650000  0.56   \n","161  13.69  3.26  2.54  20.0  107.0  1.83  0.56  0.50  0.80   5.880000  0.96   \n","162  12.85  3.27  2.58  22.0  106.0  1.65  0.60  0.60  0.96   5.580000  0.87   \n","163  12.96  3.45  2.35  18.5  106.0  1.39  0.70  0.40  0.94   5.280000  0.68   \n","164  13.78  2.76  2.30  22.0   90.0  1.35  0.68  0.41  1.03   9.580000  0.70   \n","165  13.73  4.36  2.26  22.5   88.0  1.28  0.47  0.52  1.15   6.620000  0.78   \n","166  13.45  3.70  2.60  23.0  111.0  1.70  0.92  0.43  1.46  10.680000  0.85   \n","167  12.82  3.37  2.30  19.5   88.0  1.48  0.66  0.40  0.97  10.260000  0.72   \n","168  13.58  2.58  2.69  24.5  105.0  1.55  0.84  0.39  1.54   8.660000  0.74   \n","169  13.40  4.60  2.86  25.0  112.0  1.98  0.96  0.27  1.11   8.500000  0.67   \n","170  12.20  3.03  2.32  19.0   96.0  1.25  0.49  0.40  0.73   5.500000  0.66   \n","171  12.77  2.39  2.28  19.5   86.0  1.39  0.51  0.48  0.64   9.899999  0.57   \n","172  14.16  2.51  2.48  20.0   91.0  1.68  0.70  0.44  1.24   9.700000  0.62   \n","173  13.71  5.65  2.45  20.5   95.0  1.68  0.61  0.52  1.06   7.700000  0.64   \n","174  13.40  3.91  2.48  23.0  102.0  1.80  0.75  0.43  1.41   7.300000  0.70   \n","175  13.27  4.28  2.26  20.0  120.0  1.59  0.69  0.43  1.35  10.200000  0.59   \n","176  13.17  2.59  2.37  20.0  120.0  1.65  0.68  0.53  1.46   9.300000  0.60   \n","177  14.13  4.10  2.74  24.5   96.0  2.05  0.76  0.56  1.35   9.200000  0.61   \n","\n","       11      12  0   \n","0    3.92  1065.0   0  \n","1    3.40  1050.0   0  \n","2    3.17  1185.0   0  \n","3    3.45  1480.0   0  \n","4    2.93   735.0   0  \n","5    2.85  1450.0   0  \n","6    3.58  1290.0   0  \n","7    3.58  1295.0   0  \n","8    2.85  1045.0   0  \n","9    3.55  1045.0   0  \n","10   3.17  1510.0   0  \n","11   2.82  1280.0   0  \n","12   2.90  1320.0   0  \n","13   2.73  1150.0   0  \n","14   3.00  1547.0   0  \n","15   2.88  1310.0   0  \n","16   2.65  1280.0   0  \n","17   2.57  1130.0   0  \n","18   2.82  1680.0   0  \n","19   3.36   845.0   0  \n","20   3.71   780.0   0  \n","21   3.52   770.0   0  \n","22   4.00  1035.0   0  \n","23   3.63  1015.0   0  \n","24   3.82   845.0   0  \n","25   3.20   830.0   0  \n","26   3.22  1195.0   0  \n","27   2.77  1285.0   0  \n","28   3.40   915.0   0  \n","29   3.59  1035.0   0  \n","..    ...     ...  ..  \n","148  1.62   650.0   2  \n","149  1.33   550.0   2  \n","150  1.30   500.0   2  \n","151  1.47   480.0   2  \n","152  1.33   425.0   2  \n","153  1.51   675.0   2  \n","154  1.55   640.0   2  \n","155  1.48   725.0   2  \n","156  1.64   480.0   2  \n","157  1.73   880.0   2  \n","158  1.96   660.0   2  \n","159  1.78   620.0   2  \n","160  1.58   520.0   2  \n","161  1.82   680.0   2  \n","162  2.11   570.0   2  \n","163  1.75   675.0   2  \n","164  1.68   615.0   2  \n","165  1.75   520.0   2  \n","166  1.56   695.0   2  \n","167  1.75   685.0   2  \n","168  1.80   750.0   2  \n","169  1.92   630.0   2  \n","170  1.83   510.0   2  \n","171  1.63   470.0   2  \n","172  1.71   660.0   2  \n","173  1.74   740.0   2  \n","174  1.56   750.0   2  \n","175  1.56   835.0   2  \n","176  1.62   840.0   2  \n","177  1.60   560.0   2  \n","\n","[178 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"JP_qct_Sp8Uw","colab_type":"code","outputId":"8629b84a-bde5-44e5-a04d-68643886293e","executionInfo":{"status":"ok","timestamp":1545963609109,"user_tz":-480,"elapsed":729,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":245}},"source":["wine.feature_names"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['alcohol',\n"," 'malic_acid',\n"," 'ash',\n"," 'alcalinity_of_ash',\n"," 'magnesium',\n"," 'total_phenols',\n"," 'flavanoids',\n"," 'nonflavanoid_phenols',\n"," 'proanthocyanins',\n"," 'color_intensity',\n"," 'hue',\n"," 'od280/od315_of_diluted_wines',\n"," 'proline']"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"p_xuLHeqqLHB","colab_type":"code","outputId":"2e4f0b68-789c-449c-e548-1b7fd6c7d83b","executionInfo":{"status":"ok","timestamp":1545963611341,"user_tz":-480,"elapsed":727,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["wine.target_names"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['class_0', 'class_1', 'class_2'], dtype='<U7')"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"FL07GWRRnNPi","colab_type":"text"},"source":["## 分类树"]},{"cell_type":"code","metadata":{"id":"rFIqPpz1qQyJ","colab_type":"code","colab":{}},"source":["Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zJ0SxEWBqaB4","colab_type":"code","outputId":"570814d3-6ae8-4f24-eebd-da655a646417","executionInfo":{"status":"ok","timestamp":1545963659597,"user_tz":-480,"elapsed":720,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["Xtrain.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(124, 13)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"b3NAbtyiqdTo","colab_type":"code","outputId":"71548614-a893-44e2-d90e-f4df3e429cc8","executionInfo":{"status":"ok","timestamp":1545963661561,"user_tz":-480,"elapsed":729,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["Xtest.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(54, 13)"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"6ePWTL9RqhdT","colab_type":"code","outputId":"f5f7b36c-8d93-4b99-a877-072526217ea6","executionInfo":{"status":"ok","timestamp":1545963664633,"user_tz":-480,"elapsed":912,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["clf = tree.DecisionTreeClassifier(criterion=\"entropy\")\n","clf = clf.fit(Xtrain, Ytrain)\n","score = clf.score(Xtest, Ytest) #返回预测的准确度\n","score"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9074074074074074"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"v1Hv1ayWnbJL","colab_type":"text"},"source":["## 画树"]},{"cell_type":"code","metadata":{"id":"xsoz9F7Dqnuv","colab_type":"code","outputId":"8f9767c7-f2b9-4ab9-cf46-dd8e45accf8f","executionInfo":{"status":"ok","timestamp":1545963701825,"user_tz":-480,"elapsed":1388,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":776}},"source":["feature_name = ['酒精','苹果酸','灰','灰的碱性','镁','总酚','类黄酮','非黄烷类酚类','花青素','颜色强度','色调','od280/od315稀释葡萄酒','脯氨酸']\n","import graphviz\n","dot_data = tree.export_graphviz(clf\n","                                ,out_file = None\n","                                ,feature_names= feature_name\n","                                ,class_names=[\"琴酒\",\"雪莉\",\"贝尔摩德\"]\n","                                ,filled=True #填充颜色\n","                                ,rounded=True\n","                                )\n","graph = graphviz.Source(dot_data)\n","graph"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<graphviz.files.Source at 0x7f852df30dd8>"],"image/svg+xml":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"1086pt\" height=\"552pt\"\n viewBox=\"0.00 0.00 1086.00 552.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 548)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-548 1082,-548 1082,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#39e581\" fill-opacity=\"0.109804\" stroke=\"#000000\" d=\"M566.5,-544C566.5,-544 450.5,-544 450.5,-544 444.5,-544 438.5,-538 438.5,-532 438.5,-532 438.5,-473 438.5,-473 438.5,-467 444.5,-461 450.5,-461 450.5,-461 566.5,-461 566.5,-461 572.5,-461 578.5,-467 578.5,-473 578.5,-473 578.5,-532 578.5,-532 578.5,-538 572.5,-544 566.5,-544\"/>\n<text text-anchor=\"middle\" x=\"508.5\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">脯氨酸 &lt;= 755.0</text>\n<text text-anchor=\"middle\" x=\"508.5\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.556</text>\n<text text-anchor=\"middle\" x=\"508.5\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 124</text>\n<text text-anchor=\"middle\" x=\"508.5\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [42, 51, 31]</text>\n<text text-anchor=\"middle\" x=\"508.5\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#39e581\" fill-opacity=\"0.407843\" stroke=\"#000000\" d=\"M478,-425C478,-425 275,-425 275,-425 269,-425 263,-419 263,-413 263,-413 263,-354 263,-354 263,-348 269,-342 275,-342 275,-342 478,-342 478,-342 484,-342 490,-348 490,-354 490,-354 490,-413 490,-413 490,-419 484,-425 478,-425\"/>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">od280/od315稀释葡萄酒 &lt;= 2.19</text>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.037</text>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 77</text>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 48, 28]</text>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M462.3328,-460.8796C452.0429,-451.6031 441.044,-441.6874 430.4713,-432.1559\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"432.6352,-429.3944 422.8642,-425.2981 427.9481,-434.5935 432.6352,-429.3944\"/>\n<text text-anchor=\"middle\" x=\"424.1573\" y=\"-446.5646\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n</g>\n<!-- 12 -->\n<g id=\"node13\" class=\"node\">\n<title>12</title>\n<path fill=\"#e58139\" fill-opacity=\"0.862745\" stroke=\"#000000\" d=\"M716,-425C716,-425 617,-425 617,-425 611,-425 605,-419 605,-413 605,-413 605,-354 605,-354 605,-348 611,-342 617,-342 617,-342 716,-342 716,-342 722,-342 728,-348 728,-354 728,-354 728,-413 728,-413 728,-419 722,-425 716,-425\"/>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">类黄酮 &lt;= 2.165</text>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.679</text>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 47</text>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [41, 3, 3]</text>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 琴酒</text>\n</g>\n<!-- 0&#45;&gt;12 -->\n<g id=\"edge12\" class=\"edge\">\n<title>0&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M563.7607,-460.8796C576.5099,-451.2774 590.1683,-440.9903 603.2291,-431.1534\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"605.3883,-433.9089 611.2705,-425.0969 601.1769,-428.3174 605.3883,-433.9089\"/>\n<text text-anchor=\"middle\" x=\"607.785\" y=\"-446.1528\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#8139e5\" fill-opacity=\"0.784314\" stroke=\"#000000\" d=\"M249.5,-306C249.5,-306 139.5,-306 139.5,-306 133.5,-306 127.5,-300 127.5,-294 127.5,-294 127.5,-235 127.5,-235 127.5,-229 133.5,-223 139.5,-223 139.5,-223 249.5,-223 249.5,-223 255.5,-223 261.5,-229 261.5,-235 261.5,-235 261.5,-294 261.5,-294 261.5,-300 255.5,-306 249.5,-306\"/>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">灰的碱性 &lt;= 17.25</text>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.672</text>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 34</text>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 6, 28]</text>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 1&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>1&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M312.8452,-341.8796C297.8824,-332.0962 281.8323,-321.6019 266.5309,-311.5971\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"268.4039,-308.6401 258.1188,-306.0969 264.5731,-314.4989 268.4039,-308.6401\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<path fill=\"#39e581\" fill-opacity=\"0.976471\" stroke=\"#000000\" d=\"M426,-306C426,-306 327,-306 327,-306 321,-306 315,-300 315,-294 315,-294 315,-235 315,-235 315,-229 321,-223 327,-223 327,-223 426,-223 426,-223 432,-223 438,-229 438,-235 438,-235 438,-294 438,-294 438,-300 432,-306 426,-306\"/>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">脯氨酸 &lt;= 726.5</text>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.159</text>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 43</text>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 42, 0]</text>\n<text text-anchor=\"middle\" x=\"376.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 1&#45;&gt;7 -->\n<g id=\"edge7\" class=\"edge\">\n<title>1&#45;&gt;7</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M376.5,-341.8796C376.5,-333.6838 376.5,-324.9891 376.5,-316.5013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"380.0001,-316.298 376.5,-306.2981 373.0001,-316.2981 380.0001,-316.298\"/>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#39e581\" stroke=\"#000000\" d=\"M103,-179.5C103,-179.5 12,-179.5 12,-179.5 6,-179.5 0,-173.5 0,-167.5 0,-167.5 0,-123.5 0,-123.5 0,-117.5 6,-111.5 12,-111.5 12,-111.5 103,-111.5 103,-111.5 109,-111.5 115,-117.5 115,-123.5 115,-123.5 115,-167.5 115,-167.5 115,-173.5 109,-179.5 103,-179.5\"/>\n<text text-anchor=\"middle\" x=\"57.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"57.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n<text text-anchor=\"middle\" x=\"57.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 5, 0]</text>\n<text text-anchor=\"middle\" x=\"57.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M146.584,-222.8796C133.0385,-211.1138 118.3092,-198.3197 104.8002,-186.5855\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"106.8503,-183.7303 97.0054,-179.8149 102.2599,-189.015 106.8503,-183.7303\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#8139e5\" fill-opacity=\"0.964706\" stroke=\"#000000\" d=\"M244,-187C244,-187 145,-187 145,-187 139,-187 133,-181 133,-175 133,-175 133,-116 133,-116 133,-110 139,-104 145,-104 145,-104 244,-104 244,-104 250,-104 256,-110 256,-116 256,-116 256,-175 256,-175 256,-181 250,-187 244,-187\"/>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">类黄酮 &lt;= 1.49</text>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.216</text>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 29</text>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 1, 28]</text>\n<text text-anchor=\"middle\" x=\"194.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M194.5,-222.8796C194.5,-214.6838 194.5,-205.9891 194.5,-197.5013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"198.0001,-197.298 194.5,-187.2981 191.0001,-197.2981 198.0001,-197.298\"/>\n</g>\n<!-- 5 -->\n<g id=\"node6\" class=\"node\">\n<title>5</title>\n<path fill=\"#8139e5\" stroke=\"#000000\" d=\"M180,-68C180,-68 81,-68 81,-68 75,-68 69,-62 69,-56 69,-56 69,-12 69,-12 69,-6 75,0 81,0 81,0 180,0 180,0 186,0 192,-6 192,-12 192,-12 192,-56 192,-56 192,-62 186,-68 180,-68\"/>\n<text text-anchor=\"middle\" x=\"130.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"130.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 28</text>\n<text text-anchor=\"middle\" x=\"130.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 0, 28]</text>\n<text text-anchor=\"middle\" x=\"130.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 4&#45;&gt;5 -->\n<g id=\"edge5\" class=\"edge\">\n<title>4&#45;&gt;5</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M170.6688,-103.9815C165.6044,-95.1585 160.2475,-85.8258 155.1532,-76.9506\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"158.1807,-75.1942 150.167,-68.2637 152.1097,-78.6789 158.1807,-75.1942\"/>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<path fill=\"#39e581\" stroke=\"#000000\" d=\"M313,-68C313,-68 222,-68 222,-68 216,-68 210,-62 210,-56 210,-56 210,-12 210,-12 210,-6 216,0 222,0 222,0 313,0 313,0 319,0 325,-6 325,-12 325,-12 325,-56 325,-56 325,-62 319,-68 313,-68\"/>\n<text text-anchor=\"middle\" x=\"267.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"267.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"267.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 1, 0]</text>\n<text text-anchor=\"middle\" x=\"267.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 4&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>4&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M221.6825,-103.9815C227.5192,-95.0666 233.6966,-85.6313 239.5614,-76.6734\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"242.5179,-78.5472 245.0673,-68.2637 236.6614,-74.7129 242.5179,-78.5472\"/>\n</g>\n<!-- 8 -->\n<g id=\"node9\" class=\"node\">\n<title>8</title>\n<path fill=\"#39e581\" stroke=\"#000000\" d=\"M385,-179.5C385,-179.5 286,-179.5 286,-179.5 280,-179.5 274,-173.5 274,-167.5 274,-167.5 274,-123.5 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350.683,-189.5677\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"353.8894,-188.1294 347.3228,-179.8149 347.2712,-190.4096 353.8894,-188.1294\"/>\n</g>\n<!-- 9 -->\n<g id=\"node10\" class=\"node\">\n<title>9</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M518,-187C518,-187 427,-187 427,-187 421,-187 415,-181 415,-175 415,-175 415,-116 415,-116 415,-110 421,-104 427,-104 427,-104 518,-104 518,-104 524,-104 530,-110 530,-116 530,-116 530,-175 530,-175 530,-181 524,-187 518,-187\"/>\n<text text-anchor=\"middle\" x=\"472.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">灰 &lt;= 2.41</text>\n<text text-anchor=\"middle\" x=\"472.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.0</text>\n<text text-anchor=\"middle\" x=\"472.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2</text>\n<text text-anchor=\"middle\" x=\"472.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 1, 0]</text>\n<text text-anchor=\"middle\" x=\"472.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 琴酒</text>\n</g>\n<!-- 7&#45;&gt;9 -->\n<g id=\"edge9\" class=\"edge\">\n<title>7&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M410.0761,-222.8796C417.2691,-213.9633 424.9384,-204.4565 432.351,-195.268\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"435.2258,-197.2788 438.7805,-187.2981 429.7776,-192.8836 435.2258,-197.2788\"/>\n</g>\n<!-- 10 -->\n<g id=\"node11\" class=\"node\">\n<title>10</title>\n<path fill=\"#39e581\" stroke=\"#000000\" d=\"M450,-68C450,-68 359,-68 359,-68 353,-68 347,-62 347,-56 347,-56 347,-12 347,-12 347,-6 353,0 359,0 359,0 450,0 450,0 456,0 462,-6 462,-12 462,-12 462,-56 462,-56 462,-62 456,-68 450,-68\"/>\n<text text-anchor=\"middle\" x=\"404.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"404.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"404.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 1, 0]</text>\n<text text-anchor=\"middle\" x=\"404.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 9&#45;&gt;10 -->\n<g id=\"edge10\" class=\"edge\">\n<title>9&#45;&gt;10</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M447.1793,-103.9815C441.7984,-95.1585 436.1068,-85.8258 430.6941,-76.9506\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"433.5912,-74.9789 425.3962,-68.2637 427.6149,-78.6236 433.5912,-74.9789\"/>\n</g>\n<!-- 11 -->\n<g id=\"node12\" class=\"node\">\n<title>11</title>\n<path fill=\"#e58139\" stroke=\"#000000\" 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512.4616,-76.9506\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"515.513,-78.6656 517.5257,-68.2637 509.4656,-75.1402 515.513,-78.6656\"/>\n</g>\n<!-- 13 -->\n<g id=\"node14\" class=\"node\">\n<title>13</title>\n<path fill=\"#8139e5\" fill-opacity=\"0.666667\" stroke=\"#000000\" d=\"M721.5,-306C721.5,-306 611.5,-306 611.5,-306 605.5,-306 599.5,-300 599.5,-294 599.5,-294 599.5,-235 599.5,-235 599.5,-229 605.5,-223 611.5,-223 611.5,-223 721.5,-223 721.5,-223 727.5,-223 733.5,-229 733.5,-235 733.5,-235 733.5,-294 733.5,-294 733.5,-300 727.5,-306 721.5,-306\"/>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">颜色强度 &lt;= 4.405</text>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.811</text>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 4</text>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 1, 3]</text>\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 12&#45;&gt;13 -->\n<g id=\"edge13\" class=\"edge\">\n<title>12&#45;&gt;13</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M666.5,-341.8796C666.5,-333.6838 666.5,-324.9891 666.5,-316.5013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"670.0001,-316.298 666.5,-306.2981 663.0001,-316.2981 670.0001,-316.298\"/>\n</g>\n<!-- 16 -->\n<g id=\"node17\" class=\"node\">\n<title>16</title>\n<path fill=\"#e58139\" fill-opacity=\"0.952941\" stroke=\"#000000\" d=\"M934.5,-306C934.5,-306 824.5,-306 824.5,-306 818.5,-306 812.5,-300 812.5,-294 812.5,-294 812.5,-235 812.5,-235 812.5,-229 818.5,-223 824.5,-223 824.5,-223 934.5,-223 934.5,-223 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d=\"M728.1084,-349.0803C751.6352,-335.9362 778.7395,-320.7934 803.4547,-306.9854\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"805.4183,-309.8976 812.4412,-301.9648 802.0042,-303.7866 805.4183,-309.8976\"/>\n</g>\n<!-- 14 -->\n<g id=\"node15\" class=\"node\">\n<title>14</title>\n<path fill=\"#39e581\" stroke=\"#000000\" d=\"M651,-179.5C651,-179.5 560,-179.5 560,-179.5 554,-179.5 548,-173.5 548,-167.5 548,-167.5 548,-123.5 548,-123.5 548,-117.5 554,-111.5 560,-111.5 560,-111.5 651,-111.5 651,-111.5 657,-111.5 663,-117.5 663,-123.5 663,-123.5 663,-167.5 663,-167.5 663,-173.5 657,-179.5 651,-179.5\"/>\n<text text-anchor=\"middle\" x=\"605.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"605.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"605.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 1, 0]</text>\n<text text-anchor=\"middle\" x=\"605.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 13&#45;&gt;14 -->\n<g id=\"edge14\" class=\"edge\">\n<title>13&#45;&gt;14</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M645.1652,-222.8796C639.5285,-211.8835 633.4315,-199.9893 627.7478,-188.9015\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"630.7663,-187.1173 623.09,-179.8149 624.5371,-190.3105 630.7663,-187.1173\"/>\n</g>\n<!-- 15 -->\n<g id=\"node16\" class=\"node\">\n<title>15</title>\n<path fill=\"#8139e5\" stroke=\"#000000\" d=\"M792,-179.5C792,-179.5 693,-179.5 693,-179.5 687,-179.5 681,-173.5 681,-167.5 681,-167.5 681,-123.5 681,-123.5 681,-117.5 687,-111.5 693,-111.5 693,-111.5 792,-111.5 792,-111.5 798,-111.5 804,-117.5 804,-123.5 804,-123.5 804,-167.5 804,-167.5 804,-173.5 798,-179.5 792,-179.5\"/>\n<text text-anchor=\"middle\" x=\"742.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"742.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3</text>\n<text text-anchor=\"middle\" x=\"742.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 0, 3]</text>\n<text text-anchor=\"middle\" x=\"742.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 13&#45;&gt;15 -->\n<g id=\"edge15\" class=\"edge\">\n<title>13&#45;&gt;15</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M693.0811,-222.8796C700.174,-211.7735 707.8521,-199.7513 714.9937,-188.5691\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"718.1518,-190.1267 720.5846,-179.8149 712.2523,-186.3589 718.1518,-190.1267\"/>\n</g>\n<!-- 17 -->\n<g 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-->\n<g id=\"edge17\" class=\"edge\">\n<title>16&#45;&gt;17</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M879.5,-222.8796C879.5,-212.2134 879.5,-200.7021 879.5,-189.9015\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"883.0001,-189.8149 879.5,-179.8149 876.0001,-189.815 883.0001,-189.8149\"/>\n</g>\n<!-- 18 -->\n<g id=\"node19\" class=\"node\">\n<title>18</title>\n<path fill=\"#e58139\" stroke=\"#000000\" d=\"M1066,-179.5C1066,-179.5 967,-179.5 967,-179.5 961,-179.5 955,-173.5 955,-167.5 955,-167.5 955,-123.5 955,-123.5 955,-117.5 961,-111.5 967,-111.5 967,-111.5 1066,-111.5 1066,-111.5 1072,-111.5 1078,-117.5 1078,-123.5 1078,-123.5 1078,-167.5 1078,-167.5 1078,-173.5 1072,-179.5 1066,-179.5\"/>\n<text text-anchor=\"middle\" x=\"1016.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"1016.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 41</text>\n<text text-anchor=\"middle\" x=\"1016.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [41, 0, 0]</text>\n<text text-anchor=\"middle\" x=\"1016.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 琴酒</text>\n</g>\n<!-- 16&#45;&gt;18 -->\n<g id=\"edge18\" class=\"edge\">\n<title>16&#45;&gt;18</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M927.416,-222.8796C940.9615,-211.1138 955.6908,-198.3197 969.1998,-186.5855\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"971.7401,-189.015 976.9946,-179.8149 967.1497,-183.7303 971.7401,-189.015\"/>\n</g>\n</g>\n</svg>\n"},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"-SrRlzYXq0bo","colab_type":"code","outputId":"619f417f-602c-4fda-f10a-30913f8b4b4f","executionInfo":{"status":"ok","timestamp":1545963715833,"user_tz":-480,"elapsed":902,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":245}},"source":["#特征重要性\n","clf.feature_importances_\n","[*zip(feature_name,clf.feature_importances_)]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('酒精', 0.0),\n"," ('苹果酸', 0.0),\n"," ('灰', 0.010364346545510945),\n"," ('灰的碱性', 0.0859334543136864),\n"," ('镁', 0.0),\n"," ('总酚', 0.0),\n"," ('类黄酮', 0.12052136391871153),\n"," ('非黄烷类酚类', 0.0),\n"," ('花青素', 0.0),\n"," ('颜色强度', 0.07729141352958586),\n"," ('色调', 0.0),\n"," ('od280/od315稀释葡萄酒', 0.25987743544234265),\n"," ('脯氨酸', 0.4460119862501626)]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"2SH3lBn7noVG","colab_type":"text"},"source":["## random_state"]},{"cell_type":"markdown","metadata":{"id":"GT2U9lX8n08h","colab_type":"text"},"source":["random_state用来设置分枝中的随机模式的参数，默认None，在高维度时随机性会表现更明显，低维度的数据（比如鸢尾花数据集），随机性几乎不会显现"]},{"cell_type":"code","metadata":{"id":"2GgbhPToq4bf","colab_type":"code","outputId":"5c6919f2-1dc9-4a15-ec36-95a59c210842","executionInfo":{"status":"ok","timestamp":1545963749345,"user_tz":-480,"elapsed":922,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=30)\n","clf = clf.fit(Xtrain, Ytrain)\n","score = clf.score(Xtest, Ytest) #返回预测的准确度\n","score"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9259259259259259"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"La1lVoUOtAOt","colab_type":"code","outputId":"32df1513-f8a0-492c-ff79-89f1d348362b","executionInfo":{"status":"ok","timestamp":1545963806047,"user_tz":-480,"elapsed":476,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["clf = tree.DecisionTreeClassifier(criterion=\"entropy\"\n","                                  ,random_state=2 #固定每次运算的结果\n","                                  ,splitter=\"random\"\n","                                  )\n","clf = clf.fit(Xtrain, Ytrain)\n","score = clf.score(Xtest, Ytest)\n","score\n","\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9259259259259259"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"NyG83E4Mv2h5","colab_type":"text"},"source":["splitter也是用来控制决策树中的随机选项的，有两种输入值，输入”best\"，决策树在分枝时虽然随机，但是还是会\n","优先选择更重要的特征进行分枝（重要性可以通过属性feature_importances_查看），输入“random\"，决策树在\n","分枝时会更加随机，树会因为含有更多的不必要信息而更深更大，并因这些不必要信息而降低对训练集的拟合。这\n","也是防止过拟合的一种方式。当你预测到你的模型会过拟合，用这两个参数来帮助你降低树建成之后过拟合的可能\n","性。当然，树一旦建成，我们依然是使用剪枝参数来防止过拟合"]},{"cell_type":"code","metadata":{"id":"-LWngrhYtJyV","colab_type":"code","outputId":"0875ebbe-0993-4928-e6f5-ba69df95d486","executionInfo":{"status":"ok","timestamp":1545963812921,"user_tz":-480,"elapsed":703,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":935}},"source":["import graphviz\n","dot_data = tree.export_graphviz(clf\n","                                ,feature_names= feature_name\n","                                ,class_names=[\"琴酒\",\"雪莉\",\"贝尔摩德\"]\n","                                ,filled=True\n","                                ,rounded=True\n","                                )\n","graph = graphviz.Source(dot_data)\n","graph"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<graphviz.files.Source at 0x7f852df30198>"],"image/svg+xml":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"1094pt\" height=\"671pt\"\n viewBox=\"0.00 0.00 1094.00 671.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 667)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-667 1090,-667 1090,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#39e581\" fill-opacity=\"0.109804\" stroke=\"#000000\" d=\"M639,-663C639,-663 428,-663 428,-663 422,-663 416,-657 416,-651 416,-651 416,-592 416,-592 416,-586 422,-580 428,-580 428,-580 639,-580 639,-580 645,-580 651,-586 651,-592 651,-592 651,-651 651,-651 651,-657 645,-663 639,-663\"/>\n<text text-anchor=\"middle\" x=\"533.5\" y=\"-647.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">od280/od315稀释葡萄酒 &lt;= 2.329</text>\n<text text-anchor=\"middle\" x=\"533.5\" y=\"-632.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.556</text>\n<text text-anchor=\"middle\" x=\"533.5\" y=\"-617.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 124</text>\n<text text-anchor=\"middle\" x=\"533.5\" y=\"-602.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [42, 51, 31]</text>\n<text text-anchor=\"middle\" x=\"533.5\" y=\"-587.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#8139e5\" fill-opacity=\"0.600000\" stroke=\"#000000\" d=\"M452,-544C452,-544 345,-544 345,-544 339,-544 333,-538 333,-532 333,-532 333,-473 333,-473 333,-467 339,-461 345,-461 345,-461 452,-461 452,-461 458,-461 464,-467 464,-473 464,-473 464,-532 464,-532 464,-538 458,-544 452,-544\"/>\n<text text-anchor=\"middle\" x=\"398.5\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">类黄酮 &lt;= 0.809</text>\n<text text-anchor=\"middle\" x=\"398.5\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.863</text>\n<text text-anchor=\"middle\" x=\"398.5\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" 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class=\"edge\">\n<title>7&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M228.1931,-103.9815C233.6531,-95.1585 239.4284,-85.8258 244.9207,-76.9506\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"248.0104,-78.609 250.2965,-68.2637 242.058,-74.9254 248.0104,-78.609\"/>\n</g>\n<!-- 13 -->\n<g id=\"node14\" class=\"node\">\n<title>13</title>\n<path fill=\"#39e581\" fill-opacity=\"0.788235\" stroke=\"#000000\" d=\"M731.5,-425C731.5,-425 621.5,-425 621.5,-425 615.5,-425 609.5,-419 609.5,-413 609.5,-413 609.5,-354 609.5,-354 609.5,-348 615.5,-342 621.5,-342 621.5,-342 731.5,-342 731.5,-342 737.5,-342 743.5,-348 743.5,-354 743.5,-354 743.5,-413 743.5,-413 743.5,-419 737.5,-425 731.5,-425\"/>\n<text text-anchor=\"middle\" x=\"676.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">颜色强度 &lt;= 3.173</text>\n<text text-anchor=\"middle\" x=\"676.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" 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fill=\"#000000\">samples = 23</text>\n<text text-anchor=\"middle\" x=\"609.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 23, 0]</text>\n<text text-anchor=\"middle\" x=\"609.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 13&#45;&gt;14 -->\n<g id=\"edge14\" class=\"edge\">\n<title>13&#45;&gt;14</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M653.0667,-341.8796C646.8137,-330.7735 640.0448,-318.7513 633.749,-307.5691\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"636.7761,-305.8116 628.8202,-298.8149 630.6765,-309.2459 636.7761,-305.8116\"/>\n</g>\n<!-- 15 -->\n<g id=\"node16\" class=\"node\">\n<title>15</title>\n<path fill=\"#39e581\" fill-opacity=\"0.466667\" stroke=\"#000000\" d=\"M800,-306C800,-306 701,-306 701,-306 695,-306 689,-300 689,-294 689,-294 689,-235 689,-235 689,-229 695,-223 701,-223 701,-223 800,-223 800,-223 806,-223 812,-229 812,-235 812,-235 812,-294 812,-294 812,-300 806,-306 800,-306\"/>\n<text text-anchor=\"middle\" x=\"750.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">镁 &lt;= 91.986</text>\n<text text-anchor=\"middle\" x=\"750.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.143</text>\n<text text-anchor=\"middle\" x=\"750.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 22</text>\n<text text-anchor=\"middle\" x=\"750.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [7, 14, 1]</text>\n<text text-anchor=\"middle\" x=\"750.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 13&#45;&gt;15 -->\n<g id=\"edge15\" class=\"edge\">\n<title>13&#45;&gt;15</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M702.3816,-341.8796C707.8142,-333.1434 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font-size=\"14.00\" fill=\"#000000\">samples = 11</text>\n<text text-anchor=\"middle\" x=\"678.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 10, 1]</text>\n<text text-anchor=\"middle\" x=\"678.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 15&#45;&gt;16 -->\n<g id=\"edge16\" class=\"edge\">\n<title>15&#45;&gt;16</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M725.3179,-222.8796C720.0867,-214.2335 714.5195,-205.0322 709.1177,-196.1042\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"711.9608,-194.0421 703.7896,-187.2981 705.9717,-197.6658 711.9608,-194.0421\"/>\n</g>\n<!-- 19 -->\n<g id=\"node20\" class=\"node\">\n<title>19</title>\n<path fill=\"#e58139\" fill-opacity=\"0.427451\" stroke=\"#000000\" d=\"M869.5,-187C869.5,-187 775.5,-187 775.5,-187 769.5,-187 763.5,-181 763.5,-175 763.5,-175 763.5,-116 763.5,-116 763.5,-110 769.5,-104 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max_depth\n","限制树的最大深度，超过设定深度的树枝全部剪掉\n","这是用得最广泛的剪枝参数，在高维度低样本量时非常有效。决策树多生长一层，对样本量的需求会增加一倍，所\n","以限制树深度能够有效地限制过拟合。在集成算法中也非常实用。实际使用时，建议从=3开始尝试，看看拟合的效\n","果再决定是否增加设定深度"]},{"cell_type":"markdown","metadata":{"id":"ybGnZxeIoNup","colab_type":"text"},"source":["### min_samples_leaf\n","限定，一个节点在分枝后的每个子节点都必须包含至少min_samples_leaf个训练样本，否则分\n","枝就不会发生，或者，分枝会朝着满足每个子节点都包含min_samples_leaf个样本的方向去发生"]},{"cell_type":"markdown","metadata":{"id":"_CeZd6HOoN4A","colab_type":"text"},"source":["### min_samples_split\n","限定，一个节点必须要包含至少min_samples_split个训练样本，这个节点才允许被分枝，否则\n","分枝就不会发生"]},{"cell_type":"code","metadata":{"id":"9dMa3RcAv9gI","colab_type":"code","outputId":"6b7a40ff-2b11-4872-e3de-48d533c724c0","executionInfo":{"status":"ok","timestamp":1545963832518,"user_tz":-480,"elapsed":719,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["clf = tree.DecisionTreeClassifier(criterion=\"entropy\"\n","                              ,random_state=2\n","                              ,splitter=\"random\"\n","                              ,max_depth=3\n","                              ,min_samples_leaf=10\n","                              ,min_samples_split=10\n","                              )\n","clf = clf.fit(Xtrain, Ytrain)\n","score = clf.score(Xtest, Ytest)\n","score\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8703703703703703"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"apFovJdJwFm6","colab_type":"code","outputId":"a0e46a4a-c899-40e0-de3d-9ecff6186db1","executionInfo":{"status":"ok","timestamp":1545963837243,"user_tz":-480,"elapsed":937,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":597}},"source":["dot_data = tree.export_graphviz(clf\n","                                ,feature_names= feature_name\n","                                ,class_names=[\"琴酒\",\"雪莉\",\"贝尔摩德\"]\n","                                ,filled=True\n","                                ,rounded=True\n","                                )\n","graph = graphviz.Source(dot_data)\n","graph"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<graphviz.files.Source at 0x7f8531431518>"],"image/svg+xml":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"854pt\" height=\"433pt\"\n viewBox=\"0.00 0.00 854.00 433.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 429)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-429 850,-429 850,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#39e581\" fill-opacity=\"0.109804\" stroke=\"#000000\" d=\"M539,-425C539,-425 328,-425 328,-425 322,-425 316,-419 316,-413 316,-413 316,-354 316,-354 316,-348 322,-342 328,-342 328,-342 539,-342 539,-342 545,-342 551,-348 551,-354 551,-354 551,-413 551,-413 551,-419 545,-425 539,-425\"/>\n<text text-anchor=\"middle\" x=\"433.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">od280/od315稀释葡萄酒 &lt;= 2.329</text>\n<text text-anchor=\"middle\" x=\"433.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.556</text>\n<text text-anchor=\"middle\" x=\"433.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 124</text>\n<text text-anchor=\"middle\" x=\"433.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [42, 51, 31]</text>\n<text text-anchor=\"middle\" x=\"433.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#8139e5\" fill-opacity=\"0.600000\" stroke=\"#000000\" d=\"M409,-306C409,-306 302,-306 302,-306 296,-306 290,-300 290,-294 290,-294 290,-235 290,-235 290,-229 296,-223 302,-223 302,-223 409,-223 409,-223 415,-223 421,-229 421,-235 421,-235 421,-294 421,-294 421,-300 415,-306 409,-306\"/>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">类黄酮 &lt;= 0.809</text>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.863</text>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 42</text>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 12, 30]</text>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M406.2194,-341.8796C400.4932,-333.1434 394.3954,-323.8404 388.4863,-314.8253\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"391.3063,-312.7429 382.8971,-306.2981 385.4518,-316.5803 391.3063,-312.7429\"/>\n<text text-anchor=\"middle\" x=\"377.8032\" y=\"-327.0736\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n</g>\n<!-- 8 -->\n<g id=\"node9\" class=\"node\">\n<title>8</title>\n<path fill=\"#e58139\" fill-opacity=\"0.070588\" stroke=\"#000000\" d=\"M569,-306C569,-306 456,-306 456,-306 450,-306 444,-300 444,-294 444,-294 444,-235 444,-235 444,-229 450,-223 456,-223 456,-223 569,-223 569,-223 575,-223 581,-229 581,-235 581,-235 581,-294 581,-294 581,-300 575,-306 569,-306\"/>\n<text text-anchor=\"middle\" x=\"512.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">脯氨酸 &lt;= 496.772</text>\n<text text-anchor=\"middle\" x=\"512.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.082</text>\n<text text-anchor=\"middle\" x=\"512.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 82</text>\n<text text-anchor=\"middle\" x=\"512.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [42, 39, 1]</text>\n<text text-anchor=\"middle\" x=\"512.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 琴酒</text>\n</g>\n<!-- 0&#45;&gt;8 -->\n<g id=\"edge8\" class=\"edge\">\n<title>0&#45;&gt;8</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M461.1304,-341.8796C466.93,-333.1434 473.1059,-323.8404 479.0908,-314.8253\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"482.1368,-316.5652 484.7517,-306.2981 476.3049,-312.6935 482.1368,-316.5652\"/>\n<text text-anchor=\"middle\" x=\"489.7022\" y=\"-327.103\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#8139e5\" fill-opacity=\"0.949020\" stroke=\"#000000\" d=\"M248,-187C248,-187 149,-187 149,-187 143,-187 137,-181 137,-175 137,-175 137,-116 137,-116 137,-110 143,-104 149,-104 149,-104 248,-104 248,-104 254,-104 260,-110 260,-116 260,-116 260,-175 260,-175 260,-181 254,-187 248,-187\"/>\n<text text-anchor=\"middle\" x=\"198.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">酒精 &lt;= 12.884</text>\n<text text-anchor=\"middle\" x=\"198.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.286</text>\n<text text-anchor=\"middle\" x=\"198.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 20</text>\n<text text-anchor=\"middle\" x=\"198.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 1, 19]</text>\n<text text-anchor=\"middle\" x=\"198.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 1&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>1&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M300.589,-222.8796C287.9206,-213.2774 274.3485,-202.9903 261.3705,-193.1534\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"263.4636,-190.3482 253.38,-187.0969 259.2352,-195.9268 263.4636,-190.3482\"/>\n</g>\n<!-- 5 -->\n<g id=\"node6\" class=\"node\">\n<title>5</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M421,-187C421,-187 290,-187 290,-187 284,-187 278,-181 278,-175 278,-175 278,-116 278,-116 278,-110 284,-104 290,-104 290,-104 421,-104 421,-104 427,-104 433,-110 433,-116 433,-116 433,-175 433,-175 433,-181 427,-187 421,-187\"/>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">非黄烷类酚类 &lt;= 0.44</text>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.0</text>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 22</text>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 11, 11]</text>\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 1&#45;&gt;5 -->\n<g id=\"edge5\" class=\"edge\">\n<title>1&#45;&gt;5</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M355.5,-222.8796C355.5,-214.6838 355.5,-205.9891 355.5,-197.5013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"359.0001,-197.298 355.5,-187.2981 352.0001,-197.2981 359.0001,-197.298\"/>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#8139e5\" fill-opacity=\"0.890196\" stroke=\"#000000\" d=\"M111,-68C111,-68 12,-68 12,-68 6,-68 0,-62 0,-56 0,-56 0,-12 0,-12 0,-6 6,0 12,0 12,0 111,0 111,0 117,0 123,-6 123,-12 123,-12 123,-56 123,-56 123,-62 117,-68 111,-68\"/>\n<text text-anchor=\"middle\" x=\"61.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.469</text>\n<text text-anchor=\"middle\" x=\"61.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n<text text-anchor=\"middle\" x=\"61.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 1, 9]</text>\n<text text-anchor=\"middle\" x=\"61.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M147.4863,-103.9815C135.742,-94.4232 123.2628,-84.2668 111.5595,-74.7419\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"113.565,-71.8614 103.5998,-68.2637 109.1464,-77.2906 113.565,-71.8614\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#8139e5\" stroke=\"#000000\" d=\"M252,-68C252,-68 153,-68 153,-68 147,-68 141,-62 141,-56 141,-56 141,-12 141,-12 141,-6 147,0 153,0 153,0 252,0 252,0 258,0 264,-6 264,-12 264,-12 264,-56 264,-56 264,-62 258,-68 252,-68\"/>\n<text text-anchor=\"middle\" x=\"202.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"middle\" x=\"202.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n<text text-anchor=\"middle\" x=\"202.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 0, 10]</text>\n<text text-anchor=\"middle\" x=\"202.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M199.9895,-103.9815C200.2895,-95.618 200.606,-86.7965 200.9093,-78.3409\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"204.41,-78.3828 201.2708,-68.2637 197.4145,-78.1317 204.41,-78.3828\"/>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<path fill=\"#8139e5\" fill-opacity=\"0.286275\" stroke=\"#000000\" d=\"M401,-68C401,-68 302,-68 302,-68 296,-68 290,-62 290,-56 290,-56 290,-12 290,-12 290,-6 296,0 302,0 302,0 401,0 401,0 407,0 413,-6 413,-12 413,-12 413,-56 413,-56 413,-62 407,-68 401,-68\"/>\n<text text-anchor=\"middle\" x=\"351.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.98</text>\n<text text-anchor=\"middle\" x=\"351.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 12</text>\n<text text-anchor=\"middle\" x=\"351.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 5, 7]</text>\n<text text-anchor=\"middle\" x=\"351.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 贝尔摩德</text>\n</g>\n<!-- 5&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>5&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M354.0105,-103.9815C353.7105,-95.618 353.394,-86.7965 353.0907,-78.3409\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"356.5855,-78.1317 352.7292,-68.2637 349.59,-78.3828 356.5855,-78.1317\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<path fill=\"#39e581\" fill-opacity=\"0.333333\" stroke=\"#000000\" d=\"M537.5,-68C537.5,-68 443.5,-68 443.5,-68 437.5,-68 431.5,-62 431.5,-56 431.5,-56 431.5,-12 431.5,-12 431.5,-6 437.5,0 443.5,0 443.5,0 537.5,0 537.5,0 543.5,0 549.5,-6 549.5,-12 549.5,-12 549.5,-56 549.5,-56 549.5,-62 543.5,-68 537.5,-68\"/>\n<text text-anchor=\"middle\" x=\"490.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.971</text>\n<text text-anchor=\"middle\" x=\"490.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n<text text-anchor=\"middle\" x=\"490.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 6, 4]</text>\n<text text-anchor=\"middle\" x=\"490.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = 雪莉</text>\n</g>\n<!-- 5&#45;&gt;7 -->\n<g id=\"edge7\" class=\"edge\">\n<title>5&#45;&gt;7</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M405.769,-103.9815C417.3418,-94.4232 429.6389,-84.2668 441.1713,-74.7419\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"443.5334,-77.3304 449.0148,-68.2637 439.0757,-71.9332 443.5334,-77.3304\"/>\n</g>\n<!-- 9 -->\n<g id=\"node10\" class=\"node\">\n<title>9</title>\n<path fill=\"#39e581\" stroke=\"#000000\" d=\"M562,-179.5C562,-179.5 463,-179.5 463,-179.5 457,-179.5 451,-173.5 451,-167.5 451,-167.5 451,-123.5 451,-123.5 451,-117.5 457,-111.5 463,-111.5 463,-111.5 562,-111.5 562,-111.5 568,-111.5 574,-117.5 574,-123.5 574,-123.5 574,-167.5 574,-167.5 574,-173.5 568,-179.5 562,-179.5\"/>\n<text 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Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["clf.score(Xtest,Ytest)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8703703703703703"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"S25YygWPoj4L","colab_type":"text"},"source":["## 确认最优的剪枝参数"]},{"cell_type":"code","metadata":{"id":"pwHmxe6zwZo_","colab_type":"code","outputId":"4c919ff1-4058-48c1-8618-c25e8fdac9f0","executionInfo":{"status":"ok","timestamp":1545963988557,"user_tz":-480,"elapsed":862,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":347}},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","test = []\n","for i in range(10):\n","  clf = tree.DecisionTreeClassifier(max_depth=i+1\n","                                    ,criterion=\"entropy\"\n","                                    ,random_state=30\n","                                    ,splitter=\"random\"\n","                                    )\n","  clf = clf.fit(Xtrain, Ytrain)\n","  score = clf.score(Xtest, Ytest)\n","  test.append(score)\n","plt.plot(range(1,11),test,color=\"red\",label=\"max_depth\")\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAecAAAFKCAYAAAAnj5dkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<matplotlib.figure.Figure at 0x7f852df30a20>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"bJkhr5Bs0CcA","colab_type":"code","outputId":"3eda605d-29f2-4ab3-cb78-b2a7515fe851","executionInfo":{"status":"ok","timestamp":1545964014648,"user_tz":-480,"elapsed":756,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["np.arange(1,11)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"9uF1xLbk0WZP","colab_type":"code","outputId":"1f1fd6c9-98a8-4d91-bc71-66175dd10855","executionInfo":{"status":"ok","timestamp":1545964009535,"user_tz":-480,"elapsed":723,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":192}},"source":["test"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.6296296296296297,\n"," 0.7962962962962963,\n"," 0.9444444444444444,\n"," 0.9444444444444444,\n"," 0.9074074074074074,\n"," 0.9074074074074074,\n"," 0.9074074074074074,\n"," 0.9074074074074074,\n"," 0.9074074074074074,\n"," 0.9074074074074074]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"Hz2mZnDTo0EJ","colab_type":"text"},"source":["## 重要属性和接口"]},{"cell_type":"code","metadata":{"id":"ADZxGk6n0lfS","colab_type":"code","outputId":"e91aaf08-70d1-4c38-e633-2db144606244","executionInfo":{"status":"ok","timestamp":1545964063730,"user_tz":-480,"elapsed":875,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":87}},"source":["#apply返回每个测试样本所在的叶子节点的索引\n","clf.apply(Xtest)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 4, 19,  4, 11,  7, 13, 19, 13,  6, 11, 19,  4, 11,  8, 19, 19,  4,\n","        4, 20, 13, 10,  4, 13, 19, 19, 19, 15, 13, 11, 19, 13,  4, 19,  4,\n","       13, 13, 15,  7, 19, 19,  4, 19,  4, 19,  4, 19,  4, 19, 13, 19, 13,\n","       13,  8,  4])"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"fQ-JB34No4gg","colab_type":"code","outputId":"7304d822-8990-42e9-d109-091264ac9086","executionInfo":{"status":"ok","timestamp":1545964076063,"user_tz":-480,"elapsed":676,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":70}},"source":["#predict返回每个测试样本的分类/回归结果\n","clf.predict(Xtest)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([2, 0, 2, 1, 2, 1, 0, 1, 1, 1, 0, 2, 1, 1, 0, 0, 2, 2, 1, 1, 2, 2,\n","       1, 0, 0, 0, 1, 1, 1, 0, 1, 2, 0, 2, 1, 1, 1, 2, 0, 0, 2, 0, 2, 0,\n","       2, 0, 2, 0, 1, 0, 1, 1, 1, 2])"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"2xfbkIN4o_mu","colab_type":"text"},"source":["# 回归树"]},{"cell_type":"code","metadata":{"id":"tDVVp4too7j_","colab_type":"code","colab":{}},"source":["from sklearn.datasets import load_boston\n","from sklearn.model_selection import cross_val_score\n","from sklearn.tree import DecisionTreeRegressor\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZSHhCxUXpKAs","colab_type":"code","outputId":"690e9fb5-65c0-4c53-c993-94c91ff53044","executionInfo":{"status":"ok","timestamp":1545964159010,"user_tz":-480,"elapsed":678,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":70}},"source":["boston = load_boston()\n","regressor = DecisionTreeRegressor(random_state=0)\n","cross_val_score(regressor, boston.data, boston.target, cv=10,\n","                scoring = \"neg_mean_squared_error\")\n","# 评估指标为负的MSE"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([-16.41568627, -10.61843137, -18.30176471, -55.36803922,\n","       -16.01470588, -44.70117647, -12.2148    , -91.3888    ,\n","       -57.764     , -36.8134    ])"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"Vjh-FSe2piGr","colab_type":"text"},"source":["## 实例：一维回归的图像绘制"]},{"cell_type":"code","metadata":{"id":"a6nD0e7qpPz5","colab_type":"code","colab":{}},"source":["import numpy as np\n","from sklearn.tree import DecisionTreeRegressor\n","import matplotlib.pyplot as plt"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0RLlBrlKpXlJ","colab_type":"code","colab":{}},"source":["rng = np.random.RandomState(1)\n","# 固定随机性\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rI4xz1y4TNAd","colab_type":"code","outputId":"a076f649-62da-4f96-9dde-cf5567008ff1","executionInfo":{"status":"ok","timestamp":1545975195617,"user_tz":-480,"elapsed":1035,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":192}},"source":["rng.rand(10,1)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.41919451],\n","       [0.6852195 ],\n","       [0.20445225],\n","       [0.87811744],\n","       [0.02738759],\n","       [0.67046751],\n","       [0.4173048 ],\n","       [0.55868983],\n","       [0.14038694],\n","       [0.19810149]])"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"pDRzadvETJ4a","colab_type":"code","colab":{}},"source":["X = np.sort(5 * rng.rand(80,1), axis=0)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"mZWf0jimT-4S","colab_type":"code","outputId":"5ed3d430-0311-4206-bbb7-e06dfa897406","executionInfo":{"status":"ok","timestamp":1545975379621,"user_tz":-480,"elapsed":815,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["np.sin(X).shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(80, 1)"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab_type":"code","id":"wWcUoFvdT8hO","outputId":"ae19a0fc-58b5-4137-a58a-76dacdd7df5b","executionInfo":{"status":"ok","timestamp":1545975406875,"user_tz":-480,"elapsed":918,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["y = np.sin(X).ravel()\n","# ravel 进行降维\n","y.shape\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(80,)"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"MzPFqcWGUZ3e","colab_type":"code","outputId":"0e3ff2fe-96c7-4197-eb72-6a2577d5076a","executionInfo":{"status":"ok","timestamp":1545975479864,"user_tz":-480,"elapsed":848,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":364}},"source":["plt.figure()\n","plt.scatter(X, y, s=20, edgecolor=\"black\",c=\"darkorange\", label=\"data\")"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7f448519e7f0>"]},"metadata":{"tags":[]},"execution_count":8},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe0AAAFKCAYAAAAwrQetAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6CYVF2TJX19rd1lAl8G1xyMEF6xAV5DUWKWtVVLubYGFlPeZNS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8ARCN//ACZZuBn05T8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\n","text/plain":["<matplotlib.figure.Figure at 0x7f448587acf8>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"amEwuvV2UG4Z","colab_type":"code","colab":{}},"source":["y[::5] += 3 * (0.5 - rng.rand(16))\n","#np.random.rand(数组结构)，生成随机数组的函数\n","#添加噪声\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uDz2VzYMpeHL","colab_type":"code","outputId":"5b8b9730-3387-4af8-99d6-02864db07886","executionInfo":{"status":"ok","timestamp":1545964244518,"user_tz":-480,"elapsed":671,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#了解降维函数ravel()的用法\n","np.random.random((2,1))\n","np.random.random((2,1)).ravel()\n","np.random.random((2,1)).ravel().shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2,)"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"4KFsUA4rpksb","colab_type":"code","outputId":"0f9360c4-d640-4082-82ab-57eac7e59b8f","executionInfo":{"status":"ok","timestamp":1545975891751,"user_tz":-480,"elapsed":730,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["regr_1 = DecisionTreeRegressor(max_depth=2)\n","regr_2 = DecisionTreeRegressor(max_depth=5)\n","regr_1.fit(X, y)\n","regr_2.fit(X, y)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n","           max_leaf_nodes=None, min_impurity_decrease=0.0,\n","           min_impurity_split=None, min_samples_leaf=1,\n","           min_samples_split=2, min_weight_fraction_leaf=0.0,\n","           presort=False, random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"Yi6TOOwYWNPu","colab_type":"code","outputId":"3ca4ced0-c576-428a-b618-05c453e5fdaf","executionInfo":{"status":"ok","timestamp":1545975953572,"user_tz":-480,"elapsed":973,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":799}},"source":["np.arange(0.0, 5.0, 0.01)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ,\n","       0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21,\n","       0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32,\n","       0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43,\n","       0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54,\n","       0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,\n","       0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,\n","       0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,\n","       0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,\n","       0.99, 1.  , 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09,\n","       1.1 , 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18, 1.19, 1.2 ,\n","       1.21, 1.22, 1.23, 1.24, 1.25, 1.26, 1.27, 1.28, 1.29, 1.3 , 1.31,\n","       1.32, 1.33, 1.34, 1.35, 1.36, 1.37, 1.38, 1.39, 1.4 , 1.41, 1.42,\n","       1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5 , 1.51, 1.52, 1.53,\n","       1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6 , 1.61, 1.62, 1.63, 1.64,\n","       1.65, 1.66, 1.67, 1.68, 1.69, 1.7 , 1.71, 1.72, 1.73, 1.74, 1.75,\n","       1.76, 1.77, 1.78, 1.79, 1.8 , 1.81, 1.82, 1.83, 1.84, 1.85, 1.86,\n","       1.87, 1.88, 1.89, 1.9 , 1.91, 1.92, 1.93, 1.94, 1.95, 1.96, 1.97,\n","       1.98, 1.99, 2.  , 2.01, 2.02, 2.03, 2.04, 2.05, 2.06, 2.07, 2.08,\n","       2.09, 2.1 , 2.11, 2.12, 2.13, 2.14, 2.15, 2.16, 2.17, 2.18, 2.19,\n","       2.2 , 2.21, 2.22, 2.23, 2.24, 2.25, 2.26, 2.27, 2.28, 2.29, 2.3 ,\n","       2.31, 2.32, 2.33, 2.34, 2.35, 2.36, 2.37, 2.38, 2.39, 2.4 , 2.41,\n","       2.42, 2.43, 2.44, 2.45, 2.46, 2.47, 2.48, 2.49, 2.5 , 2.51, 2.52,\n","       2.53, 2.54, 2.55, 2.56, 2.57, 2.58, 2.59, 2.6 , 2.61, 2.62, 2.63,\n","       2.64, 2.65, 2.66, 2.67, 2.68, 2.69, 2.7 , 2.71, 2.72, 2.73, 2.74,\n","       2.75, 2.76, 2.77, 2.78, 2.79, 2.8 , 2.81, 2.82, 2.83, 2.84, 2.85,\n","       2.86, 2.87, 2.88, 2.89, 2.9 , 2.91, 2.92, 2.93, 2.94, 2.95, 2.96,\n","       2.97, 2.98, 2.99, 3.  , 3.01, 3.02, 3.03, 3.04, 3.05, 3.06, 3.07,\n","       3.08, 3.09, 3.1 , 3.11, 3.12, 3.13, 3.14, 3.15, 3.16, 3.17, 3.18,\n","       3.19, 3.2 , 3.21, 3.22, 3.23, 3.24, 3.25, 3.26, 3.27, 3.28, 3.29,\n","       3.3 , 3.31, 3.32, 3.33, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.4 ,\n","       3.41, 3.42, 3.43, 3.44, 3.45, 3.46, 3.47, 3.48, 3.49, 3.5 , 3.51,\n","       3.52, 3.53, 3.54, 3.55, 3.56, 3.57, 3.58, 3.59, 3.6 , 3.61, 3.62,\n","       3.63, 3.64, 3.65, 3.66, 3.67, 3.68, 3.69, 3.7 , 3.71, 3.72, 3.73,\n","       3.74, 3.75, 3.76, 3.77, 3.78, 3.79, 3.8 , 3.81, 3.82, 3.83, 3.84,\n","       3.85, 3.86, 3.87, 3.88, 3.89, 3.9 , 3.91, 3.92, 3.93, 3.94, 3.95,\n","       3.96, 3.97, 3.98, 3.99, 4.  , 4.01, 4.02, 4.03, 4.04, 4.05, 4.06,\n","       4.07, 4.08, 4.09, 4.1 , 4.11, 4.12, 4.13, 4.14, 4.15, 4.16, 4.17,\n","       4.18, 4.19, 4.2 , 4.21, 4.22, 4.23, 4.24, 4.25, 4.26, 4.27, 4.28,\n","       4.29, 4.3 , 4.31, 4.32, 4.33, 4.34, 4.35, 4.36, 4.37, 4.38, 4.39,\n","       4.4 , 4.41, 4.42, 4.43, 4.44, 4.45, 4.46, 4.47, 4.48, 4.49, 4.5 ,\n","       4.51, 4.52, 4.53, 4.54, 4.55, 4.56, 4.57, 4.58, 4.59, 4.6 , 4.61,\n","       4.62, 4.63, 4.64, 4.65, 4.66, 4.67, 4.68, 4.69, 4.7 , 4.71, 4.72,\n","       4.73, 4.74, 4.75, 4.76, 4.77, 4.78, 4.79, 4.8 , 4.81, 4.82, 4.83,\n","       4.84, 4.85, 4.86, 4.87, 4.88, 4.89, 4.9 , 4.91, 4.92, 4.93, 4.94,\n","       4.95, 4.96, 4.97, 4.98, 4.99])"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"59pro3mYWVIe","colab_type":"code","outputId":"2d9439fe-aca2-40c5-cc56-190724b19949","executionInfo":{"status":"ok","timestamp":1545975989234,"user_tz":-480,"elapsed":789,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["np.arange(0.0, 5.0, 0.01).shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(500,)"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"_BnfSWh1WZz3","colab_type":"code","outputId":"b3598259-8c1e-44c0-98a5-46ba5aaa0414","executionInfo":{"status":"ok","timestamp":1545976010749,"user_tz":-480,"elapsed":744,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":8517}},"source":["np.arange(0.0, 5.0, 0.01)[:, np.newaxis]\n","# 增维"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.  ],\n","       [0.01],\n","       [0.02],\n","       [0.03],\n","       [0.04],\n","       [0.05],\n","       [0.06],\n","       [0.07],\n","       [0.08],\n","       [0.09],\n","       [0.1 ],\n","       [0.11],\n","       [0.12],\n","       [0.13],\n","       [0.14],\n","       [0.15],\n","       [0.16],\n","       [0.17],\n","  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1)"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"1VeQrAe6WkS2","colab_type":"code","outputId":"5c267363-c9b9-4b0e-c00c-1a42f301caca","executionInfo":{"status":"ok","timestamp":1545976048967,"user_tz":-480,"elapsed":720,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["np.arange(0.0, 5.0, 0.01)[np.newaxis,:].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1, 500)"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"X13NMjx7poyo","colab_type":"code","colab":{}},"source":["X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]\n","y_1 = regr_1.predict(X_test)\n","y_2 = regr_2.predict(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"KLKTPNz8puBD","colab_type":"code","outputId":"395fac7a-db80-4d5d-bc31-fa93e421168b","executionInfo":{"status":"ok","timestamp":1545964297718,"user_tz":-480,"elapsed":832,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":376}},"source":["plt.figure()\n","plt.scatter(X, y, s=20, edgecolor=\"black\",c=\"darkorange\", label=\"data\")\n","plt.plot(X_test, y_1, color=\"cornflowerblue\",label=\"max_depth=2\", linewidth=2)\n","plt.plot(X_test, y_2, color=\"yellowgreen\", label=\"max_depth=5\", linewidth=2)\n","plt.xlabel(\"data\")\n","plt.ylabel(\"target\")\n","plt.title(\"Decision Tree Regression\")\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbbfddhsjR44kLS2NOXPmsHHjRiZOnNjs84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DqPxBrzOPD3euxdR3HxDkr2n1wl9HvQjhI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\nEZQ6AAAe8U/PEeX8g8qszwAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7f852deceef0>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"eDuYW7Xlp5Bb","colab_type":"text"},"source":["# 实例：泰坦尼克号幸存者的预测"]},{"cell_type":"code","metadata":{"id":"5Ttq2W7Up4Hv","colab_type":"code","colab":{}},"source":["import pandas as pd\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.model_selection import train_test_split\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.model_selection import cross_val_score\n","import matplotlib.pyplot as plt"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qDi59Iwdpxor","colab_type":"code","colab":{}},"source":["data = pd.read_csv(r\"./train.csv\")\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zn_QBoKJqB3E","colab_type":"code","outputId":"82762204-4823-4c71-b67a-fd4248258fbb","executionInfo":{"status":"ok","timestamp":1545964493065,"user_tz":-480,"elapsed":722,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":213}},"source":["data.head()\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. Laina</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Allen, Mr. William Henry</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   PassengerId  Survived  Pclass  \\\n","0            1         0       3   \n","1            2         1       1   \n","2            3         1       3   \n","3            4         1       1   \n","4            5         0       3   \n","\n","                                                Name     Sex   Age  SibSp  \\\n","0                            Braund, Mr. Owen Harris    male  22.0      1   \n","1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n","2                             Heikkinen, Miss. Laina  female  26.0      0   \n","3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n","4                           Allen, Mr. William Henry    male  35.0      0   \n","\n","   Parch            Ticket     Fare Cabin Embarked  \n","0      0         A/5 21171   7.2500   NaN        S  \n","1      0          PC 17599  71.2833   C85        C  \n","2      0  STON/O2. 3101282   7.9250   NaN        S  \n","3      0            113803  53.1000  C123        S  \n","4      0            373450   8.0500   NaN        S  "]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"D6FXJR6nqCgD","colab_type":"code","outputId":"aca1aef0-5f4d-4b34-e2a5-0dff39382072","executionInfo":{"status":"ok","timestamp":1545964495891,"user_tz":-480,"elapsed":652,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":334}},"source":["data.info()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n","PassengerId    891 non-null int64\n","Survived       891 non-null int64\n","Pclass         891 non-null int64\n","Name           891 non-null object\n","Sex            891 non-null object\n","Age            714 non-null float64\n","SibSp          891 non-null int64\n","Parch          891 non-null int64\n","Ticket         891 non-null object\n","Fare           891 non-null float64\n","Cabin          204 non-null object\n","Embarked       889 non-null object\n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.6+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"qsE7pSmEqiD3","colab_type":"code","colab":{}},"source":["#删除缺失值过多的列，和观察判断来说和预测的y没有关系的列\n","data.drop([\"Cabin\",\"Name\",\"Ticket\"],inplace=True,axis=1)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"Oz6IGnh2rWUO","colab":{}},"source":["#处理缺失值，对缺失值较多的列进行填补，有一些特征只确实一两个值，可以采取直接删除记录的方法\n","data[\"Age\"] = data[\"Age\"].fillna(data[\"Age\"].mean())\n","data = data.dropna()\n","# 默认axis =0 删除含有缺失值的行\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3AcLG-sWxMSK","colab_type":"text"},"source":["## 数据转换成数值型变量"]},{"cell_type":"code","metadata":{"id":"v0ynSO3jrXSH","colab_type":"code","colab":{}},"source":["#将分类变量转换为数值型变量\n","#将二分类变量转换为数值型变量\n","#astype能够将一个pandas对象转换为某种类型，和apply(int(x))不同，astype可以将文本类转换为数字，用这个方式可以很便捷地将二分类特征转换为0~1\n","data[\"Sex\"] = (data[\"Sex\"]== \"male\").astype(\"int\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TQnKk_BWrZNy","colab_type":"code","colab":{}},"source":["#将三分类变量转换为数值型变量\n","labels = data[\"Embarked\"].unique().tolist()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tzp1QlR6rgG1","colab_type":"code","colab":{}},"source":["data[\"Embarked\"] = data[\"Embarked\"].apply(lambda x: labels.index(x))\n","                                          "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rwCGoNJaqupU","colab_type":"code","outputId":"428f5947-9cb6-4022-f9bc-2c48a6d1d62f","executionInfo":{"status":"ok","timestamp":1545964783694,"user_tz":-480,"elapsed":667,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":226}},"source":["#查看处理后的数据集\n","data.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n","0            1         0       3    1  22.0      1      0   7.2500         0\n","1            2         1       1    0  38.0      1      0  71.2833         1\n","2            3         1       3    0  26.0      0      0   7.9250         0\n","3            4         1       1    0  35.0      1      0  53.1000         0\n","4            5         0       3    1  35.0      0      0   8.0500         0"]},"metadata":{"tags":[]},"execution_count":57}]},{"cell_type":"code","metadata":{"id":"MRJ3SP9zroU4","colab_type":"code","colab":{}},"source":["X = data.iloc[:,data.columns != \"Survived\"]\n","y = data.iloc[:,data.columns == \"Survived\"]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"H81qeDCnrsZ-","colab_type":"code","outputId":"28390d12-3804-47eb-e400-344a1246eca0","executionInfo":{"status":"ok","timestamp":1545964829196,"user_tz":-480,"elapsed":674,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":226}},"source":["from sklearn.model_selection import train_test_split\n","Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y,test_size=0.3)\n","#修正测试集和训练集的索引\n","for i in [Xtrain, Xtest, Ytrain, Ytest]:\n","    i.index = range(i.shape[0])\n","#查看分好的训练集和测试集\n","Xtrain.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Pclass</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>804</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.42</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>8.5167</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>24</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>28.00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>35.5000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>643</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>2.00</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>27.9000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>772</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>48.00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.8542</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>483</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>50.00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   PassengerId  Pclass  Sex    Age  SibSp  Parch     Fare  Embarked\n","0          804       3    1   0.42      0      1   8.5167         1\n","1           24       1    1  28.00      0      0  35.5000         0\n","2          643       3    0   2.00      3      2  27.9000         0\n","3          772       3    1  48.00      0      0   7.8542         0\n","4          483       3    1  50.00      0      0   8.0500         0"]},"metadata":{"tags":[]},"execution_count":59}]},{"cell_type":"code","metadata":{"id":"a9Nyh8zWrzbp","colab_type":"code","outputId":"514afeed-4295-46a1-96ee-948eb1b0d765","executionInfo":{"status":"ok","timestamp":1545964858355,"user_tz":-480,"elapsed":735,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":37}},"source":["clf = DecisionTreeClassifier(random_state=25)\n","clf = clf.fit(Xtrain, Ytrain)\n","score_ = clf.score(Xtest, Ytest)\n","score_\n","\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.7715355805243446"]},"metadata":{"tags":[]},"execution_count":60}]},{"cell_type":"code","metadata":{"id":"BKk_k_WQr5xW","colab_type":"code","outputId":"5facbc9d-c868-4fce-8fae-d2599719bd98","executionInfo":{"status":"ok","timestamp":1545964867629,"user_tz":-480,"elapsed":925,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":37}},"source":["score = cross_val_score(clf,X,y,cv=10).mean()\n","score\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.7469611848825333"]},"metadata":{"tags":[]},"execution_count":61}]},{"cell_type":"code","metadata":{"id":"17mWFrDgr8MA","colab_type":"code","outputId":"51de0978-e093-43e9-b5ec-a4212a224bb6","executionInfo":{"status":"ok","timestamp":1545964904695,"user_tz":-480,"elapsed":1585,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":367}},"source":["tr = []\n","te = []\n","for i in range(10):\n","    clf = DecisionTreeClassifier(random_state=25\n","                                ,max_depth=i+1\n","                                ,criterion=\"entropy\"\n","                                )\n","    clf = clf.fit(Xtrain, Ytrain)\n","    score_tr = clf.score(Xtrain,Ytrain)\n","    score_te = cross_val_score(clf,X,y,cv=10).mean()\n","    tr.append(score_tr)\n","    te.append(score_te)\n","print(max(te))\n","plt.plot(range(1,11),tr,color=\"red\",label=\"train\")\n","plt.plot(range(1,11),te,color=\"blue\",label=\"test\")\n","plt.xticks(range(1,11))\n","plt.legend()\n","plt.show()\n","#这里为什么使用“entropy”？因为我们注意到，在最大深度=3的时候，模型拟合不足，在训练集和测试集上的表现接近，但却都不是非常理想，只能够达到83%左右，所以我们要使用entropy。"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.8166624106230849\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe0AAAFKCAYAAAAwrQetAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7f852b59b240>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"Q-d4eejpsJcV","colab_type":"text"},"source":["## 用网格搜索调整参数"]},{"cell_type":"code","metadata":{"id":"EXwUpbJzsFo3","colab_type":"code","outputId":"69667e31-f173-4940-b634-98bbd6760c0b","executionInfo":{"status":"ok","timestamp":1545965261103,"user_tz":-480,"elapsed":310109,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":309}},"source":["import numpy as np\n","gini_thresholds = np.linspace(0,0.5,20)\n","parameters = {'splitter':('best','random')\n","              ,'criterion':(\"gini\",\"entropy\")\n","              ,\"max_depth\":[*range(1,10)]\n","              ,'min_samples_leaf':[*range(1,50,5)]\n","              ,'min_impurity_decrease':[*np.linspace(0,0.5,20)]\n","              }\n","clf = DecisionTreeClassifier(random_state=25)\n","GS = GridSearchCV(clf, parameters, cv=10)\n","GS.fit(Xtrain,Ytrain)\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n","  DeprecationWarning)\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=10, error_score='raise-deprecating',\n","       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n","            max_features=None, max_leaf_nodes=None,\n","            min_impurity_decrease=0.0, min_impurity_split=None,\n","            min_samples_leaf=1, min_samples_split=2,\n","            min_weight_fraction_leaf=0.0, presort=False, random_state=25,\n","            splitter='best'),\n","       fit_params=None, iid='warn', n_jobs=None,\n","       param_grid={'splitter': ('best', 'random'), 'criterion': ('gini', 'entropy'), 'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9], 'min_samples_leaf': [1, 6, 11, 16, 21, 26, 31, 36, 41, 46], 'min_impurity_decrease': [0.0, 0.02631578947368421, 0.05263157894736842, 0.07894736842105263, 0.10526315789473684, 0.131...0526315789, 0.39473684210526316, 0.42105263157894735, 0.4473684210526315, 0.47368421052631576, 0.5]},\n","       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n","       scoring=None, verbose=0)"]},"metadata":{"tags":[]},"execution_count":63}]},{"cell_type":"code","metadata":{"colab_type":"code","id":"XroP_O6YsUnq","outputId":"f6b55889-4680-459f-89b2-10289c43d4b7","executionInfo":{"status":"ok","timestamp":1545965384779,"user_tz":-480,"elapsed":738,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":121}},"source":["GS.best_params_\n","#返回最佳组合"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'criterion': 'gini',\n"," 'max_depth': 4,\n"," 'min_impurity_decrease': 0.0,\n"," 'min_samples_leaf': 1,\n"," 'splitter': 'random'}"]},"metadata":{"tags":[]},"execution_count":64}]},{"cell_type":"code","metadata":{"id":"YaWRK5sdsVeP","colab_type":"code","outputId":"7b2c4e59-e70e-42cc-b3ed-6a4b7df9d30e","executionInfo":{"status":"ok","timestamp":1545965388089,"user_tz":-480,"elapsed":645,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":37}},"source":["GS.best_score_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8311897106109325"]},"metadata":{"tags":[]},"execution_count":65}]},{"cell_type":"code","metadata":{"id":"dj9C2EWft748","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3Mhl6P-Xf-Na","colab_type":"text"},"source":["# I 实例：分类树在合成数集上的表现"]},{"cell_type":"code","metadata":{"id":"Ix_ad0cnf_v1","colab_type":"code","colab":{}},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from matplotlib.colors import ListedColormap\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.datasets import make_moons, make_circles, make_classification\n","from sklearn.tree import DecisionTreeClassifier"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BrC_BjncgECA","colab_type":"code","colab":{}},"source":["#make_classification库生成随机的二分型数据\n","X, y = make_classification(n_samples=100, #生成100个样本\n","                            n_features=2, #包含2个特征，即生成二维数据\n","                            n_redundant=0, #添加冗余特征0个\n","                            n_informative=2, #包含信息的特征是2个\n","                            random_state=1, #随机模式1\n","                            n_clusters_per_class=1 #每个簇内包含的标签类别有1个\n","                            )                         "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"h2nMo5z9gV1E","colab_type":"code","outputId":"ae0b2ab0-0217-4b8e-b1c3-cb589d70e61c","executionInfo":{"status":"ok","timestamp":1546243109883,"user_tz":-480,"elapsed":682,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":364}},"source":["#在这里可以查看一下X和y，其中X是100行带有两个2特征的数据，y是二分类标签\n","#也可以画出散点图来观察一下X中特征的分布\n","plt.scatter(X[:,0],X[:,1])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7f66215f16d8>"]},"metadata":{"tags":[]},"execution_count":3},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe4AAAFKCAYAAADbmryuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBflWXAkOi6a3OOOpJUpesi1LlDkM3Clic6c6qG3zj4mLs0y8Tu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at 0x7f66252b4a90>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"xSyJElbZgZa-","colab_type":"code","outputId":"b04b4a31-269b-4ffe-b67c-a7c896ed4362","executionInfo":{"status":"ok","timestamp":1546243114156,"user_tz":-480,"elapsed":715,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":364}},"source":["#从图上可以看出，生成的二分型数据的两个簇离彼此很远，这样不利于我们测试分类器的效果，因此我们使用np生成\n","# 随机数组，通过让已经生成的二分型数据点加减0~1之间的随机数，使数据分布变得更散更稀疏\n","#注意，这个过程只能够运行一次，因为多次运行之后X会变得非常稀疏，两个簇的数据会混合在一起，分类器的效应会继续下降\n","rng = np.random.RandomState(2) #生成一种随机模式\n","X += 2 * rng.uniform(size=X.shape) #加减0~1之间的随机数\n","linearly_separable = (X, y) #生成了新的X，依然可以画散点图来观察一下特征的分布\n","plt.scatter(X[:,0],X[:,1])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7f661ed1d358>"]},"metadata":{"tags":[]},"execution_count":4},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAeEAAAFKCAYAAAAqkecjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmDC1btK0+XSNPczFL4l/QZRTEN69e7c+/vhj3bhxQ88++6weee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mNfOsxKJ/dbLgfreOl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\n4BKCMAAALvn/P3/cuRxqsEMAAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7f661edc3940>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"McKIUbX8ghvF","colab_type":"code","colab":{}},"source":["#用make_moons创建月亮型数据，make_circles创建环形数据，并将三组数据打包起来放在列表datasets中\n","datasets = [make_moons(noise=0.3, random_state=0),\n","            make_circles(noise=0.2, factor=0.5, random_state=1),\n","            linearly_separable]\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"SoovLQiVgqje","colab_type":"code","outputId":"660b906d-d46c-419e-c78e-cf2b8c4139cd","executionInfo":{"status":"ok","timestamp":1546243135512,"user_tz":-480,"elapsed":1800,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":653}},"source":["#创建画布，宽高比为6*9\n","figure = plt.figure(figsize=(6, 9))\n","#设置用来安排图像显示位置的全局变量i\n","i = 1\n","#开始迭代数据，对datasets中的数据进行for循环\n","for ds_index, ds in enumerate(datasets):\n","    #对X中的数据进行标准化处理，然后分训练集和测试集\n","    X, y = ds\n","    X = StandardScaler().fit_transform(X)\n","    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4,\n","    random_state=42)\n","    #找出数据集中两个特征的最大值和最小值，让最大值+0.5，最小值-0.5，创造一个比两个特征的区间本身更大一点的区间\n","    x1_min, x1_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n","    x2_min, x2_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n","    #用特征向量生成网格数据，网格数据，其实就相当于坐标轴上无数个点\n","    #函数np.arange在给定的两个数之间返回均匀间隔的值，0.2为步长\n","    #函数meshgrid用以生成网格数据，能够将两个一维数组生成两个二维矩阵。\n","    #如果第一个数组是narray，维度是n，第二个参数是marray，维度是m。那么生成的第一个二维数组是以\n","    #   narray为行，m行的矩阵，而第二个二维数组是以marray的转置为列，n列的矩阵\n","    #生成的网格数据，是用来绘制决策边界的，因为绘制决策边界的函数contourf要求输入的两个特征都必须是二维的\n","    array1,array2 = np.meshgrid(np.arange(x1_min, x1_max, 0.2),\n","    np.arange(x2_min, x2_max, 0.2))\n","    #接下来生成彩色画布\n","    #用ListedColormap为画布创建颜色，#FF0000正红，#0000FF正蓝\n","    cm = plt.cm.RdBu\n","    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n","    #在画布上加上一个子图，数据为len(datasets)行，2列，放在位置i上\n","    ax = plt.subplot(len(datasets), 2, i)\n","    #到这里为止，已经生成了0~1之间的坐标系3个了，接下来为我们的坐标系放上标题\n","    #我们有三个坐标系，但我们只需要在第一个坐标系上有标题，因此设定if ds_index==0这个条件\n","    if ds_index == 0:\n","        ax.set_title(\"Input data\")\n","    #将数据集的分布放到我们的坐标系上\n","    #先放训练集\n","    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train,\n","              cmap=cm_bright,edgecolors='k')\n","    #放测试集\n","    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test,\n","              cmap=cm_bright, alpha=0.6,edgecolors='k')\n","    #为图设置坐标轴的最大值和最小值，并设定没有坐标轴\n","    ax.set_xlim(array1.min(), array1.max())\n","    ax.set_ylim(array2.min(), array2.max())\n","    ax.set_xticks(())\n","    ax.set_yticks(())\n","    #每次循环之后，改变i的取值让图每次位列不同的位置\n","    i += 1\n","    #至此为止，数据集本身的图像已经布置完毕，运行以上的代码，可以看见三个已经处理好的数据集\n","    #############################从这里开始是决策树模型##########################\n","    #迭代决策树，首先用subplot增加子图，subplot(行，列，索引)这样的结构，并使用索引i定义图的位置\n","    #在这里，len(datasets)其实就是3，2是两列\n","    #在函数最开始，我们定义了i=1，并且在上边建立数据集的图像的时候，已经让i+1,所以i在每次循环中的取值是2，4，6\n","    ax = plt.subplot(len(datasets),2,i)\n","    #决策树的建模过程：实例化 → fit训练 → score接口得到预测的准确率\n","    clf = DecisionTreeClassifier(max_depth=5)\n","    clf.fit(X_train, y_train)\n","    score = clf.score(X_test, y_test)\n","    #绘制决策边界，为此，我们将为网格中的每个点指定一种颜色[x1_min，x1_max] x [x2_min，x2_max]\n","    #分类树的接口，predict_proba，返回每一个输入的数据点所对应的标签类概率\n","    #类概率是数据点所在的叶节点中相同类的样本数量/叶节点中的样本总数量\n","    #由于决策树在训练的时候导入的训练集X_train里面包含两个特征，所以我们在计算类概率的时候，也必须导入结构相同的数组，即是说，必须有两个特征\n","    #ravel()能够将一个多维数组转换成一维数组\n","    #np.c_是能够将两个数组组合起来的函数\n","\n","    #在这里，我们先将两个网格数据降维降维成一维数组，再将两个数组链接变成含有两个特征的数据，再带入决策\n","    #   树模型，生成的Z包含数据的索引和每个样本点对应的类概率，再切片，切出类概率\n","    Z = clf.predict_proba(np.c_[array1.ravel(),array2.ravel()])[:, 1]\n","    #np.c_[np.array([1,2,3]), np.array([4,5,6])]\n","    #将返回的类概率作为数据，放到contourf里面绘制去绘制轮廓\n","    Z = Z.reshape(array1.shape)\n","    ax.contourf(array1, array2, Z, cmap=cm, alpha=.8)\n","    #将数据集的分布放到我们的坐标系上\n","    # 将训练集放到图中去\n","    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n","             edgecolors='k')\n","    # 将测试集放到图中去\n","    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n","              edgecolors='k', alpha=0.6)\n","    #为图设置坐标轴的最大值和最小值\n","    ax.set_xlim(array1.min(), array1.max())\n","    ax.set_ylim(array2.min(), array2.max())\n","    #设定坐标轴不显示标尺也不显示数字\n","    ax.set_xticks(())\n","    ax.set_yticks(())\n","    #我们有三个坐标系，但我们只需要在第一个坐标系上有标题，因此设定if ds_index==0这个条件\n","    if ds_index == 0:\n","        ax.set_title(\"Decision Tree\")\n","    #写在右下角的数字\n","    ax.text(array1.max() - .3, array2.min() + .3, ('{:.1f}%'.format(score*100)),\n","    size=15, horizontalalignment='right')\n","    #让i继续加一\n","    i += 1\n","plt.tight_layout()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaQAAAJ8CAYAAACxydXqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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PCPH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mDPnFX70oyuG3L/H4+GJJ/ZRWTkVpTIUWZb56qtKVq4sY8GCXNb9+Y8UPf4IN50Qt218cTVr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\nT+OTTw5x4AA4nSLJyR4u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OUKHf+lSVj38g3MuT3b4//7KjNLSoPs2jRvPJ+3jaWrWMi7H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vPw6xQACIImib7vTRKoWE1BqIpEYutlRrRSk4X70kZ4YIep2SZ9rTpcbLMvZYmHjp1iIKC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\ngCRJkiTlBRmQJEmSpLzw/we9R2iTWTQzAAAAAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7f661ed02f98>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"90mhPyQJhE9E","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}